{"id":33958,"date":"2019-08-29T05:38:50","date_gmt":"2019-08-29T03:38:50","guid":{"rendered":"http:\/\/datascience.unifi.it\/cladag2021\/?page_id=33958"},"modified":"2021-09-09T08:21:46","modified_gmt":"2021-09-09T06:21:46","slug":"conference-program","status":"publish","type":"page","link":"https:\/\/datascience.unifi.it\/cladag2021\/conference-program\/","title":{"rendered":"Conference program"},"content":{"rendered":"

[et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”hero section” _builder_version=”4.9.7″][et_pb_row admin_label=”title and illustration” _builder_version=”4.9.7″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text disabled_on=”off|off|off” _builder_version=”4.9.10″ text_font=”||on||||||” text_text_color=”#959baf” header_font=”Merriweather|700|||||||” header_text_color=”#” header_font_size=”46px” header_line_height=”1.3em” custom_margin=”||0px|” animation_style=”flip” animation_direction=”top”]<\/p>\n

Conference program<\/h1>\n

[\/et_pb_text][et_pb_divider color=”#a5b1ca” divider_position=”center” divider_weight=”2px” _builder_version=”4.9.7″ max_width=”90px” max_width_tablet=”12%” max_width_last_edited=”off|desktop” custom_margin=”|||” animation_style=”flip” animation_delay=”50ms”][\/et_pb_divider][et_pb_button button_url=”https:\/\/datascience.unifi.it\/cladag2021\/wp-content\/uploads\/2021\/08\/tentative_schedule.pdf” button_text=”Download the schedule” button_alignment=”center” disabled_on=”off|off|off” _builder_version=”4.9.10″ custom_button=”on”][\/et_pb_button][et_pb_button button_url=”https:\/\/datascience.unifi.it\/cladag2021\/wp-content\/uploads\/2021\/09\/Final-program-CLADAG-2021.pdf” button_text=”Download the final program” button_alignment=”center” disabled_on=”off|off|off” _builder_version=”4.9.10″ custom_button=”on” button_text_size=”19.5px”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”schedules section” _builder_version=”3.22″][et_pb_row _builder_version=”3.25″ custom_padding=”10px|0px|10px|0px” border_color_all=”#4646c4″ border_width_bottom=”3px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” header_font=”||||||||” header_2_font=”|700|||||||” header_2_line_height=”1.4em”]<\/p>\n

Day 1 – Thursday, 9 Sept 2021<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ disabled_on=”off|off|off” admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”10:15 \u2013 10:45″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”Opening Session ” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Opening Statements<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”4.9.7″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”11:00 – 12:15″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”3.27.4″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #1<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 1.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Time series clustering” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ content_tablet=”” content_phone=”” content_last_edited=”on|desktop” _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Michele La Rocca, Pietro Coretto<\/span><\/p>\n

\n

Clustering financial time series using generalized cross correlations (p.27)<\/b>
Andres M. Alonso, Carolina Gamboa and Daniel Pe\u00f1a
<\/span><\/span><\/p>\n

Network-based semi-supervised clustering of time series data (p.62)<\/b>
<\/span>Claudio Conversano, Giulia Contu, Luca Frigau and Carmela Cappelli\u00a0<\/span><\/p>\n

Spatial-temporal clustering based on B-splines: robust models with applications to COVID-19 pandemic (p.83)<\/b>
<\/span>Pierpaolo D’Urso, Livia De Giovanni and Vincenzina Vitale<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 1.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Modern likelihood methods for model based-clustering” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Monia Ranalli<\/p>\n

Discussant<\/span>: Roberto Rocci<\/p>\n

\n

Hidden Markov and regime switching copula models for state allocation in multiple time-series (p.36)<\/b>
Francesco Bartolucci, Fulvia Pennoni and Federico Cortese<\/span><\/p>\n

Clustering data with non-ignorable missingness using semi-parametric mixture models (p.79)<\/b>
<\/span>Marie Du Roy de Chaumaray and Matthieu Marbac<\/span><\/p>\n

Gaussian mixture models for high dimensional data using composite likelihood (p.98)<\/b>
<\/span>Michael Fop, Dimitris Karlis, Ioannis Kosmidis, Adrian O’Hagan, Caitriona Ryan and Isobel Claire Gormley<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 1.3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Robust classification in action” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Marco Riani<\/span><\/p>\n

\n

Robust issues in estimating modes for multivariate torus data (p.21)<\/b>
Claudio Agostinelli, Giovanni Saraceno and Luca Greco<\/span><\/span><\/p>\n

Robust estimation of parsimonious finite mixture of Gaussian models (p.92)<\/b>
<\/span>Luis Angel Garc\u00eda-Escudero, Agust\u00edn Mayo-Iscar and Marco Riani<\/span><\/p>\n

Robust supervised clustering: some practical issues (p.142)<\/b>
<\/span>Fabrizio Laurini and Gianluca Morelli<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 1.4″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Flexible Bayesian mixture models for complex data” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Alessandra Guglielmi<\/p>\n

\n

Prediction of large observations via Bayesian inference for extreme-value theory (p.231)<\/b>
Isadora Antoniano Villalobos, Simone Padoan and Boris Beranger<\/span><\/p>\n

Model-based clustering for categorical data via Hamming distance (p.31)<\/b>
<\/span>Raffaele Argiento, Edoardo Filippi-Mazzola and Lucia Paci<\/span><\/p>\n

MCMC computations for Bayesian mixture models using repulsive point processes (p.167)<\/b>
<\/span>Jesper M\u00f8ller, Mario Beraha, Raffaele Argiento and Alessandra Guglielmi<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”12:30 \u2013 13:30″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”KEY #1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Keynote #1 – Optimal transport methods for fairness in machine learning<\/strong><\/h3>\n

Jean-Michel Loubes<\/span><\/a>, Universit\u00e9 Toulouse Paul Sabatier (FRANCE)<\/span><\/p>\n

Chair: Francesca Grieselin<\/em><\/span><\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 5\u00a0<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”13:30 \u2013 14:00″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Lunch break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”14:00 \u2013 15:00″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Contributed sessions #1<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”4.9.7″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 1.A” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Models for clustering ” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Exploring solutions via monitoring for cluster weighted robust models (p.284)<\/b>
Andrea Cappozzo, Luis Angel Garc\u00eca Escudero, Francesca Greselin and Agust\u00ecn Mayo-Iscar<\/span><\/p>\n

Clustering production indexes for construction with forecast distributions (p.360)<\/b>
<\/span>Michele La Rocca, Francesco Giordano and Cira Perna<\/span><\/p>\n

Semi-constrained model-based clustering of mixed-type data using a composite likelihood approach (p.408)<\/b>
Roberto Rocci and Monia Ranalli<\/span><\/span><\/span><\/p>\n

Clustering models for three-way data (p.432)<\/b>
Donatella Vicari and Paolo Giordani<\/span>
<\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 1.B” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Nonparametric and semiparametric methods” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Semiparametric finite mixture of regression models with Bayesian P-splines (p.268)<\/b>
Marco Berrettini, Giuliano Galimberti and Saverio Ranciati<\/span><\/p>\n

Angular halfspace depth: classification using spherical bagdistances (p.316)<\/b>
<\/span>Houyem Demni, Davide Buttarazzi, Stanislav Nagy and Giovanni Camillo Porzio<\/span><\/p>\n

Functional cluster analysis of HDI evolution in European countries (p.336)<\/b>
Francesca Fortuna, Alessia Naccarato and Silvia Terzi<\/span><\/span><\/p>\n

A nonparametric test for mode significance (p.388)<\/b>
Giovanna Menardi and Federico Ferraccioli<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 1.C” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Data analysis in biomedical science” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

A subject-specific measure of interrater agreement based on the homogeneity index (p.272)<\/b>
Giuseppe Bove<\/span><\/p>\n

Clustering longitudinal data with category theory for diabetic kidney disease (p.364)<\/b>
<\/span>Maria Mannone, Veronica Distefano, Claudio Silvestri and Irene Poli<\/span><\/p>\n

Antibodies to SARS-CoV-2: an exploratory analysis carried out through the Bayesian profile regression (p.412)<\/b>
Annalina Sarra, Adelia Evangelista, Tonio Di Battista and Damiana Pieragostino<\/span><\/span><\/p>\n

Modelling three-way RNA sequencing data with mixture of multivariate Poisson-lognormal distribution (p.416)<\/b>
Theresa Scharl and Bettina Gr\u00fcn<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 1.D” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Modelling dependence structures ” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

An alternative to joint graphical lasso for learning multiple Gaussian graphical models (p.332)<\/b>
<\/span>Lorenzo Focardi Olmi and Anna Gottard<\/span><\/p>\n

A Bayesian framework for structural learning of mixed graphical models (p.344)<\/b>
<\/span>Chiara Galimberti, Federico Castelletti and Stefano Peluso<\/span><\/p>\n

Model selection procedure for mixture hidden Markov models (p.243<\/b>)<\/b><\/span>
<\/span>Antonino Abbruzzo, Maria Francesca Cracolici and Furio Urso<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 1.E” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Data analysis in economics and finance” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Predictive power of Bayesian CAR models on scale free networks: an application for credit risk (p.264)<\/b>
Claudia Berloco, Raffaele Argiento and Silvia Montagna<\/span><\/p>\n

A Machine Learning Approach in stock risk management (p.308)<\/b>
<\/span>Salvatore Cuomo, Federico Gatta, Fabio Giampaolo, Carmela Iorio and Francesco Piccialli<\/span><\/p>\n

Clustering income data based on share densities (p.300)<\/b>
<\/span>Francesca Condino<\/span><\/p>\n

Pathmox segmentation trees to compare linear regression models (p.312)<\/b>
<\/span>Cristina Davino and Giuseppe Lamberti<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”15:15 \u2013 16:30″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #2<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 2.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Copulas in time series analysis ” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Marta L. Di Lascio, Roberta Pappad\u00e0<\/p>\n

\n

DTW-based assessment of the predictive power of the copula-DCC-GARCH-MST model developed for European insurance institutions (p.71)<\/b>
Anna Denkowska and Stanis\u0142aw Wanat<\/span><\/p>\n

Nonlinear Interconnectedness of crude oil and financial markets (p.173)<\/b>
<\/span>Yarema Okhrin, Gazi Salah Uddin and Muhammad Yahya<\/span><\/p>\n

Assessing food security issues in Italy: a quantile copula approach (p.195)<\/b>
<\/span>Giorgia Rivieccio,<\/span> Jean-Paul Chavas, Giovanni De Luca, Salvatore Di Falco and Fabian Capitanio<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 2.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Advances in mixture models for matrix-variate and tensor data ” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair:<\/span> Antonio Punzo<\/span><\/b><\/p>\n

\n

Using Subset Log-Likelihoods to Trim Outliers in Gaussian Mixture Models<\/b>
Katharine Clark and Paul McNicholas<\/span><\/p>\n

Tensor-variate finite mixture model for the analysis of university professor remuneration (p.208)<\/b>
<\/span>Shuchismita Sarkar, Volodymyr Melnykov and Xuwen Zhu<\/span><\/p>\n

Network analysis implementing a mixture distribution from Bayesian viewpoint (p.210)<\/b>
<\/span>Jarod Smith, Mohammad Arashi and Andriette Bekker<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 2.3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Methods for inference from innovative or multiple data sources” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Emilia Rocco, Chiara Bocci<\/span><\/b><\/p>\n

\n

Improving the reliability of a nonprobability web survey (p.120)<\/b>
Yinxuan Huang and Natalie Shlomo
<\/span><\/p>\n

A nonparametric approach for statistical matching under informative sampling and nonresponse (p.146)<\/span><\/b>
Daniela Marella and Danny Pfeffermann<\/span>
<\/span><\/p>\n

Measurement errors in multiple systems estimation (p.211)<\/span><\/b>
Paul Smith, Peter van der Heijden and Maarten Cruyff<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 2.4″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Social inequalities” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair:<\/span> Mariangela Zenga<\/span><\/b><\/p>\n

\n

Socioeconomic inequalities and cancer risk: myth or reality? (p.106)<\/b>
Carlotta Galeone<\/span><\/p>\n

Quantifying the impact of covariates on the gender gap measurement: an analysis based on EU-SILC data from Poland and Italy (p.108)<\/b>
Francesca Greselin and Alina J\u0119drzejczak<\/span>
<\/span><\/p>\n

Gender inequalities from an income perspective (p.158)<\/b>
Marcella Mazzoleni, Angiola Pollastri and Vanda Tulli<\/span>
<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”16:45 \u2013 17:45″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”KEY #2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Keynote #2 – Veridical Data Science: the practice of responsible data analysis and decision-making<\/h3>\n

Bin Yu<\/span><\/a>, University of California Berkeley (USA)<\/p>\n

Chair: Maurizio Vichi<\/em><\/p>\n

CLADAG 2021 Additional Abstracts <\/a><\/span><\/span><\/p>\n

[\/et_pb_text][et_pb_toggle closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”18:00 \u2013 19:15″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”3.27.4″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #3<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ min_height=”32px” custom_padding=”0px||1px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 3.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Recent advances in item response theory models” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Silvia Cagnone <\/span><\/p>\n

\n

Investigating model fit in item response models with the Hellinger distance (p.150)
<\/span><\/b>Mariagiulia Matteucci and Stefania Mignani<\/span><\/p>\n

Boosting multidimensional IRT models (p.40)<\/b>
Michela Battauz and Paolo Vidoni<\/span><\/p>\n

A study of lack-of-fit diagnostics for models fit to cross-classified binary variables (p.191)<\/b>
<\/span>Mark Reiser and Maduranga Dassanayake<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 3.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Advances in clustering ” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair:<\/span> Luca Frigau<\/span><\/b><\/p>\n

\n

Cluster validity by random forests (p.91)<\/b>
Tahir Ekin and Claudio Conversano
<\/span><\/p>\n

Non-parametric consistency for the Gaussian mixture maximum likelihood estimator (p.116)<\/b>
<\/span>Christian Hennig and Pietro Coretto
<\/span><\/p>\n

Minimizing conflicts of interest: optimizing the JSM program (p.240)<\/b>
<\/span>Qiuyi Wu and David Banks<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||6px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 3.3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Advances in parsimonious mixture modeling” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer<\/span>: Volodymyr Melnykov<\/p>\n

Chair<\/span>: Xuwen Zhu<\/p>\n

\n

Parameter-wise co-clustering for high dimensional data (p.107)<\/b>
Michael Gallaugher, Christophe Biernacki and Paul McNicholas
<\/span><\/p>\n

Transformation mixture modeling for skewed data groups with heavy tails and scatter (p.162)<\/b>
<\/span>Yana Melnykov, Xuwen Zhu and Volodymyr Melnykov<\/span><\/p>\n

Clustering via new parsimonious mixtures of heavy tailed distributions (p.216)<\/b>
Salvatore Daniele Tomarchio, Luca Bagnato and Antonio Punzo
<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 3.4″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Bayesian non parametrics methods for classification ” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair:<\/span> Bruno Scarpa<\/span><\/b><\/p>\n

\n

Bayesian nonparametric dynamic modeling of psychological traits (p.25)<\/b>
Emanuele Aliverti<\/span><\/p>\n

Semiparametric IRT models for non-normal latent traits (p.178)<\/b>
<\/span>Sally Paganin
<\/span><\/p>\n

Malaria risk detection via mixed membership models (p.203)<\/b>
<\/span>Massimiliano Russo<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”schedules section” _builder_version=”3.27.3″ background_color=”#f5f5f5″ custom_padding=”0px||0px|||” animation_style=”zoom” animation_intensity_zoom=”10%”][et_pb_row _builder_version=”3.27.3″ custom_padding=”10px|0px|10px|0px” border_color_all=”#4646c4″ border_width_bottom=”3px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” header_font=”||||||||” header_2_font=”|700|||||||” header_2_line_height=”1.4em”]<\/p>\n

Day 2 – Friday, 10 Sept 2021<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”9:15 – 10:30″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”CLADAG ASSEMBLY” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”3.27.4″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Cladag assembly<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”10:45 \u2013 12:00″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #4<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 4.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Recent developments in symbolic data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Paula Brito<\/span><\/b><\/p>\n

\n

A generalised clusterwise regression for distributional data (p.223)<\/b>
Rosanna Verde, Francisco T. de A. De Carvalho and Antonio Balzanella<\/span>
<\/span><\/p>\n

Detection of internet attacks with histogram principal component analysis (p.174)<\/b><\/span>
M. Ros\u00e1rio Oliveira, Ana Subtil and Lina Oliveira<\/span><\/span><\/p>\n

Identifying mortality patterns of main causes of death among young EU population using SDA approaches (p.141)<\/b><\/span>
Simona Korenjak-\u010cerne and Nata\u0161a Kej\u017ear<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 4.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Recent advances in dynamic clustering: Markov models and extensions” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Daniele Tomarchio<\/p>\n

\n

Accounting for response behavior in longitudinal rating data (p.58)<\/b>
Roberto Colombi, Sabrina Giordano and Maria Kateri<\/span><\/p>\n

Modeling clusters of corporate defaults: regime-switching models significantly reduce the contagion source<\/b><\/span>
B\u00e5rd St\u00f8ve, Geir D. Berentsen, Jan Bulla and Antonello Maruotti<\/span><\/span>
<\/span><\/p>\n

Two-step estimation of multilevel latent class models with covariates (p.75)<\/b>
<\/span>Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser and Jouni Kuha<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 4.3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Networks data analysis and applications” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Mario R. Guarracino<\/p>\n

\n

Sender and receiver effects in latent space models for multiplex data (p.68)<\/b>
Silvia D’Angelo<\/span><\/p>\n

Networks of networks (p.186)<\/b>
<\/span>Panos Pardalos
<\/span><\/p>\n

Community detection in tripartite networks of university students mobility flows (p.232)<\/b>
Maria Prosperina Vitale, Vincenzo Giuseppe Genova, Giuseppe Giordano and Giancarlo Ragozini<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 4.4 ” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Recent developments in the statistical analysis of categorical data” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Claudia Tarantola<\/span><\/b><\/p>\n

\n

A risk indicator for categorical data (p.93)<\/b>
<\/span>Silvia Facchinetti and Silvia Angela Osmetti<\/span><\/p>\n

A semi-Bayesian approach for the analysis of scale effects in ordinal regression models (p.124)<\/b>
Maria Iannario and Claudia Tarantola
<\/span><\/span><\/p>\n

Simple effect measures for interpreting generalized binary regression models (p.129)<\/b><\/span>
Maria Kateri<\/span><\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”12:15 \u2013 13:30″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #5<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 5.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”New issues in univariate and multivariate quantile regression” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Lea Petrella<\/p>\n

\n

Understanding and estimating conditional parametric quantile models (p.44)<\/b>
Matteo Bottai<\/span><\/p>\n

On model-based clustering using quantile regression (p.102)<\/b>
<\/span>Carlo Gaetan, Paolo Girardi and Victor Muthama Musau<\/span><\/p>\n

Unconditional M-quantile regression (p.163)<\/b>
<\/span>Luca Merlo, Lea Petrella and Nikos Tzavidis<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 5.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Penalized techniques for data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Gianluca Sottile<\/p>\n

\n

Shapley Lorenz methods for eXplainable artificial intelligence (p.45)<\/b>
Niklas Bussmann, Roman Enzmann, Paolo Giudici and Emanuela Raffinetti
<\/span><\/p>\n

Smoothed non linear PCA for multivariate data (p.54)<\/b>
<\/span>Marcello Chiodi<\/span><\/p>\n

Causal regularization (p.236)<\/b>
<\/span>Ernst Wit and Lucas Kania<\/span>
<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 5.3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Latent variable mixture modeling in epidemiology” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizers<\/span>: Maria Iannario<\/p>\n

\n

Characterising longitudinal trajectories of COVID-19 biomarkers within a latent class framework (p.64)<\/b>
Federica Cugnata, Chiara Brombin, Pietro Cipp\u00e0, Alessandro Ceschi, Paolo Ferrari and Clelia Di Serio<\/span><\/p>\n

Pairwise likelihood estimation of latent autoregressive count models (p.187)<\/b>
<\/span>Xanthi Pedeli and Cristiano Varin
<\/span><\/p>\n

A machine learning approach for evaluating anxiety in neurosurgical patients during the COVID-19 pandemic (p.227)<\/b>
<\/span>Marika Vezzoli, Francesco Doglietto, Stefano Renzetti, Marco Fontanella and Stefano Calza<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 5.4″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Co-clustering for temporal sequences and distributional data” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Rosanna Verde<\/p>\n

\n

Mining multiple time sequences through co-clustering algorithms for distributional data (p.32)<\/b>
Antonio Balzanella, Antonio Irpino and Francisco de A.T. De Carvalho<\/span><\/p>\n

Co-clustering for high dimensional sparse data<\/b>
<\/span>Nicoleta Rogovschi<\/span><\/p>\n

A general bi-clustering technique for functional data (p.217)<\/b>
<\/span>Agostino Torti, Marta Galvani, Alessandra Menafoglio, Piercesare Secchi and Simone Vantini<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”13:30 \u2013 14:00″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Lunch break<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”14:00 \u2013 15:15″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.7″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #6<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 6.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Advances in robust cluster analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizers: Luis Angel Garc\u00eda-Escudero,\u00a0 Agust\u00edn Mayo-Iscar, Marco Riani<\/span><\/span><\/p>\n

Chair<\/span>: Agust\u00edn Mayo-Iscar<\/p>\n

\n

Robust classification of spectroscopic data in agri-food: first analysis on the stability of results (p.49)<\/b>
Andrea Cappozzo, Ludovic Duponchel, Francesca Greselin and Brendan Murphy<\/span><\/p>\n

Robust classification in high dimensions using regularized covariance estimates (p.215)<\/b>
<\/span>Valentin Todorov and Peter Filzmoser<\/span><\/p>\n

Issues in monitoring the EU trade of critical COVID-19 commodities(p.53)<\/b><\/span>
Andrea Cerasa, Enrico Checchi, Domenico Perrotta and Francesca Torti<\/span><\/span><\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 6.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Bayesian analysis of finite and infinite mixtures” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Bettina Gr\u00fcn<\/p>\n

\n

PIVMET: pivotal methods for Bayesian relabelling in finite mixture models (p.87)<\/b>
Leonardo Egidi, Roberta Pappad\u00e0, Francesco Pauli and Nicola Torelli<\/span><\/p>\n

A transdimensional MCMC sampler for spatially dependent mixture models (p.112)<\/b>
<\/span>Alessandra Guglielmi,<\/span> Mario Beraha, Matteo Giannella, Matteo Pegoraro and Riccardo Peli<\/span>\u00a0
<\/span><\/p>\n

Infinite mixtures of infinite factor analysers (p.168)<\/b>
Keefe Murphy, Cinzia Viroli and Isobel Claire Gormley<\/span>\u00a0<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 6.3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Latent variable models for constructing composite indices” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizers and Chairs<\/span>: Rosaria Romano<\/p>\n

\n

PCA-based composite indices and measurement model (p.154)<\/b>
<\/span>Matteo Mazziotta and Adriano Pareto<\/span><\/p>\n

Specifying composites in structural equation modeling: the Henseler-Ogasawara specification (p.209)<\/b>
<\/span>Florian Schuberth<\/span><\/p>\n

Developing a multidimensional and hierarchical index following a composite-based approach (p.220)<\/b>
<\/span>Laura Trinchera<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 6.4″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Issues in directional data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>: Giovanni Camillo Porzio<\/span><\/p>\n

\n

Best approach direction for spherical random variables (p.128)<\/b>
Jayant Jha<\/span><\/p>\n

Angular halfspace depth: computation (p.169)<\/b>
Stanislav Nagy, Petra Laketa and Rainer Dyckerhoff<\/span>\u00a0<\/span><\/p>\n

Nonparametric estimation of the number of clusters for directional data (p.207)<\/b>
<\/span>Paula Saavedra-Nieves and Rosa M. Crujeiras<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”15:30 \u2013 16:30″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”KEY #3″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Keynote #3 – Understanding cross-validation and prediction error<\/h3>\n

Robert Tibshirani<\/span>, Stephen Bates and Trevor Hastie
<\/span><\/p>\n

Chair: Anna Gottard<\/em>\u00a0<\/span><\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 7<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle open_toggle_background_color=”#f5f5f5″ closed_toggle_background_color=”#F5F5F5″ icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”16:45 \u2013 17:45″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”KEY #4″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Keynote #4 – Class maps for visualizing classification results<\/h3>\n

Peter Rousseeuw<\/span>, Jakob Raymaekers and Mia Hubert<\/span><\/p>\n

Chair: Marco Riani<\/em>\u00a0<\/span><\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 6<\/span><\/span><\/p>\n

[\/et_pb_text][et_pb_toggle closed_toggle_background_color=”#F5F5F5″ icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_margin=”|auto|16px|auto||” custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”18:00 – 20:00″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”PLENARY” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Plenary – Statistical Issues in the COVID-19 Pandemic<\/h3>\n

Organizer and Chair: J. Sunil Rao<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”A simple correction for COVID-19 sampling bias” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][et_pb_text _builder_version=”4.9.10″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false”]<\/p>\n

Daniel Diaz<\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 14<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”A seat at the table: the key role of biostatistics and data science in the COVID-19 pandemic” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][et_pb_text _builder_version=”4.9.10″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false”]<\/p>\n

Jeffrey S. Morris<\/span><\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 15<\/span><\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”Predictions, role of interventions and the crisis of virus in India: a data science call to arms” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Bhramar Mukherjee<\/span><\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 16<\/span><\/span><\/p>\n

[\/et_pb_toggle][et_pb_text _builder_version=”4.9.10″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false”]<\/p>\n

Bhramar Mukherjee<\/span><\/p>\n

CLADAG 2021 Book of Abstract and Short papers, p. 16<\/span><\/span><\/p>\n

[\/et_pb_text][et_pb_toggle title=”Contributions of Israel\u2019s CBS to rout COVID-19″ open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”||10px||false|false” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][et_pb_text _builder_version=”4.9.10″ custom_margin=”-12px||10px||false|false” custom_padding=”|||30px|false|false”]<\/p>\n

Danny Pfeffermann<\/span><\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 17<\/span><\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”schedules section” _builder_version=”3.22″ custom_padding=”0px||0px|||” animation_style=”zoom” animation_intensity_zoom=”10%”][et_pb_row _builder_version=”3.27.3″ custom_padding=”10px|0px|10px|0px” border_color_all=”#4646c4″ border_width_bottom=”3px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” header_font=”||||||||” header_2_font=”|700|||||||” header_2_line_height=”1.4em”]<\/p>\n

Day 3 – Saturday, 11 Sept 2021<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”10:00 \u2013 11:15″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Invited sessions #7 & Contributed sessions #2<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 7.1″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Recent advances in directional statistics” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Stefania Fensore, Agnese Panzera<\/span><\/b><\/p>\n

\n

Mixtures of Kato\u2013Jones distributions on the circle, with an application to traffic count data (p.133)<\/b>
Shogo Kato, Kota Nagasaki and Wataru Nakanishi<\/span><\/p>\n

How to design a directional distribution (p.137)<\/b>
John Kent<\/span>
<\/span><\/p>\n

A graphical depth-based aid to detect deviation from unimodality on hyperspheres (p.182)<\/b><\/span>
Giuseppe Pandolfo
<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”INV 7.2″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Recent developments in flexible regression \u2013 methods and software” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Organizer and Chair<\/span>:\u00a0Marco Geraci<\/span><\/b><\/p>\n

\n

Additive quantile regression via the qgam R package (p.97)<\/b>
Matteo Fasiolo\u00a0<\/span><\/span><\/p>\n

Additive Bayesian variable selection under censoring and misspecification<\/b><\/p>\n

Javier Rubio
<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 2.A” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Robust methods and data diagnostic ” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

A combined test of the Benford hypothesis with anti-fraud applications (p.256)<\/b>
Lucio Barabesi, Andrea Cerasa, Andrea Cerioli and Domenico Perrotta<\/span><\/span><\/p>\n

Multivariate outlier detection for histogram-valued variables (p.384)<\/b>
<\/span>Ana Martins, Paula Brito, S\u00f3nia Dias and Peter Filzmoser<\/span><\/p>\n

\u00a0Angular halfspace depth: Central regions (p.356)<\/b>
<\/span>Petra Laketa and Stanislav Nagy<\/span><\/p>\n

Robustness methods for modelling count data with general dependence structures (p.396)<\/b>
<\/span>Marta Nai Ruscone and Dimitris Karlis<\/span><\/p>\n

A robust quantile approach to ordinal trees (p.424)<\/b>
<\/span><\/span>Rosaria Simone<\/span>,<\/span><\/span> Cristina Davino, Domenico Vistocco and Gerhard Tutz<\/span>
<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 2.B” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Web mining and textual data analysis” open_toggle_background_color=”#ffffe0″ icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Unabalanced classfication of electronic invoicing (p.260)<\/b>
Chiara Bardelli<\/span><\/p>\n

The use of multiple imputation techniques for social media data (p.372)<\/b>
Paolo Mariani, Andrea Marletta and Matteo Locci<\/span><\/span><\/p>\n

Visualizing cluster of words: a graphical approach to grammar acquisition (p.392)<\/b>
Massimo Mucciardi, Giovanni Pirrotta, Andrea Briglia and Arnaud Sallaberry<\/span><\/span><\/p>\n

Using eye-traking data to create a weighted dictionary for sentiment analysis: the eye dictionary (p.436)<\/b>
Gianpaolo Zammarchi and Jaromir Antoch\u00a0<\/span><\/span><\/p>\n

<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”11:30 \u2013 12:30″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Contributed sessions #3<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 3.A” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Data analysis in biology and environmental sciences” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Model-based clustering for estimating cetaceans site-fidelity and abundance (p.292)<\/b>
Gianmarco Caruso, Greta Panunzi, Marco Mingione, Pierfrancesco Alaimo Di Loro, Stefano Moro, Edoardo Bompiani, Caterina Lanfredi, Daniela Silvia Pace, Luca Tardella and Giovanna Jona Lasinio<\/span><\/p>\n

Prediction of gene expression from transcription factors affinities: an application of Bayesian non-linear modelling (p.376)<\/b>
<\/span>Federico Marotta, Paolo Provero and Silvia Montagna<\/span><\/p>\n

High dimensional model-based clustering of European georeferenced vegetation plots (p.380)<\/b>
Francesca Martella, Fabio Attorre, Michele De Sanctis and Giuliano Fanelli<\/span><\/span><\/p>\n

Bayesian analysis of a water quality high-frequency time series through Markov switching autoregressive models (p.400)<\/b>
Roberta Paroli, Luigi Spezia, Marc Stutter and Andy Vinten<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 3.B” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Process and service quality evaluation” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” hover_enabled=”0″ border_width_all=”0px” locked=”off” sticky_enabled=”0″]<\/p>\n

Estimating latent linear correlations from fuzzy contingency tables (p.276)<\/b>
<\/span>Antonio Calcagn\u00ec<\/span><\/p>\n

The LP<\/sup> <\/em>data depth and its application to multivariate process control charts (p.352)<\/b>
<\/span>Carmela Iorio, Giuseppe Pandolfo, Michele Staiano, Massimo Aria and Roberta Siciliano<\/span><\/p>\n

Detecting the effect of secondary school in higher education university choices (p.404)<\/b>
<\/span>Mariano Porcu, Isabella Sulis and Cristian Usala<\/span><\/p>\n

 <\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 3.C” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Mixture models and random effect models” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Estimating Bayesian mixtures of finite mixtures with telescoping sampling (p.340)<\/b>
Sylvia Fr\u00fchwirth-Schnatter, Bettina Gr\u00fcn and Gertraud Malsiner-Walli<\/span><\/span><\/p>\n

Measurement error models on spatial network lattices: car crashes in Leeds (p.348)<\/b>
<\/span>Andrea Gilardi, Riccardo Borgoni, Luca Presicce and Jorge Mateu<\/span><\/p>\n

A redundancy analysis with multivariate random-coefficients linear models (p.368)<\/b>
Laura Marcis, Maria Chiara Pagliarella and Renato Salvatore<\/span><\/span><\/p>\n

Group-dependent finite mixture model (p.304)<\/b><\/span>
Paula Costa Fontichiari, Miriam Giuliani, Raffaele Argiento and Lucia Paci<\/span><\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 3.D” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Machine learning and statistical learning” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

Semi-supervised Learning through depth functions (p.255)<\/b>
Simona Balzano, Mario Rosario Guarracino and Giovanni Camillo Porzio<\/span><\/p>\n

Model-based clustering with sparse matrix mixture models (p.280)<\/b>
<\/span>Andrea Cappozzo, Alessandro Casa and Michael Fop<\/span><\/p>\n

Neural networks for high cardinality categorical data (p.320)<\/b>
<\/span>Agostino Di Ciaccio<\/span><\/p>\n

Stacking ensemble of Gaussian mixtures (p.420)<\/b>
<\/span><\/span>Luca Scrucca<\/span>
<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,1_4,1_2″ _builder_version=”3.27.3″ custom_padding=”0px||0px|||”][et_pb_column type=”1_4″ _builder_version=”3.27.3″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.27.3″][et_pb_blurb title=”CON 3.E” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.27.3″][et_pb_toggle title=”Hierarchical clustering and classification methods” open_toggle_background_color=”#ffffe0″ closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.10″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”]<\/p>\n

A full mixture of experts model to classify constrained data (p.247)<\/b>
Roberto Ascari and Sonia Migliorati<\/span><\/p>\n

Categorical classifiers in multi-class classification problems (p.288)<\/b>
<\/span>Maurizio Carpita and Silvia Golia<\/span><\/p>\n

Ali-Mikhail-Haq copula to detect low correlations in hierarchical clustering (p.324)<\/b>
<\/span>F. Marta L. Di Lascio, Andrea Menapace and Roberta Pappad\u00e0<\/span><\/p>\n

Model-based clustering with parsimonious covariance structure (p.296)<\/b><\/span>
<\/span>Carlo Cavicchia, Maurizio Vichi and Giorgia Zaccaria<\/span><\/p>\n

[\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”12:45 \u2013 13:45″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”KEY #5″ use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Keynote #5 – Quantile-based classification<\/h3>\n

Cinzia Viroli<\/a>, University of Bologna (ITALY)<\/p>\n

Chair: Brendan Murphy<\/em>\u00a0<\/p>\n

CLADAG 2021 Book of Abstracts and Short papers, p. 8<\/span><\/p>\n

[\/et_pb_text][et_pb_toggle closed_toggle_background_color=”#ffffff” icon_color=”#4646c4″ _builder_version=”4.9.7″ title_font=”||||||||” title_font_size=”18px” title_line_height=”1.8em” body_font=”||||||||” body_font_size=”15px” body_line_height=”1.8em” custom_margin=”|||” custom_padding=”0px|0px|0px|0px” border_width_all=”0px” locked=”off”][\/et_pb_toggle][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ admin_label=”Timing and speaker” _builder_version=”3.27.3″ custom_padding=”20px|0px|0px|0px|false|false” border_color_all=”#e1e3e5″ border_width_top=”1px”][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_blurb title=”13:45 \u2013 14:00″ use_icon=”on” font_icon=”%%92%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.7″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”||-5px||false|false”][\/et_pb_blurb][et_pb_blurb title=”CLOSING STATEMENTS” use_icon=”on” font_icon=”%%305%%” icon_color=”#a9aab7″ icon_placement=”left” use_icon_font_size=”on” icon_font_size=”24px” _builder_version=”4.9.10″ header_font=”|||on|||||” header_font_size=”14px” header_letter_spacing=”1px” header_line_height=”24px” body_font=”||||||||” custom_margin=”|||”][\/et_pb_blurb][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.10″ text_font=”||||||||” text_line_height=”1.8em” link_font=”||||||||” link_text_color=”#4646c4″ header_font=”||||||||” header_3_font=”Merriweather|700|||||||” header_3_text_color=”#4646c4″ header_3_line_height=”1.3em” custom_margin=”||20px|”]<\/p>\n

Closing Statements<\/a><\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

Conference program Download the schedule Download the final program Day 1 – Thursday, 9 Sept 2021 } 10:15 \u2013 10:45  Opening Session Opening Statements } 11:00 – 12:15 Invited sessions #1  INV 1.1 Time series clustering Organizer and Chair: Michele La Rocca, Pietro Coretto Clustering financial time series using generalized cross correlations (p.27)Andres […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":""},"_links":{"self":[{"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/pages\/33958"}],"collection":[{"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/comments?post=33958"}],"version-history":[{"count":430,"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/pages\/33958\/revisions"}],"predecessor-version":[{"id":36113,"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/pages\/33958\/revisions\/36113"}],"wp:attachment":[{"href":"https:\/\/datascience.unifi.it\/cladag2021\/wp-json\/wp\/v2\/media?parent=33958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}