Conference program
Day 1 – Thursday, 9 Sept 2021
10:15 – 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 M. Alonso, Carolina Gamboa and Daniel Peña
Network-based semi-supervised clustering of time series data (p.62)
Claudio Conversano, Giulia Contu, Luca Frigau and Carmela Cappelli
Spatial-temporal clustering based on B-splines: robust models with applications to COVID-19 pandemic (p.83)
Pierpaolo D’Urso, Livia De Giovanni and Vincenzina Vitale
INV 1.2
Modern likelihood methods for model based-clustering
Organizer and Chair: Monia Ranalli
Discussant: Roberto Rocci
Hidden Markov and regime switching copula models for state allocation in multiple time-series (p.36)
Francesco Bartolucci, Fulvia Pennoni and Federico Cortese
Clustering data with non-ignorable missingness using semi-parametric mixture models (p.79)
Marie Du Roy de Chaumaray and Matthieu Marbac
Gaussian mixture models for high dimensional data using composite likelihood (p.98)
Michael Fop, Dimitris Karlis, Ioannis Kosmidis, Adrian O’Hagan, Caitriona Ryan and Isobel Claire Gormley
INV 1.3
Robust classification in action
Organizer and Chair: Marco Riani
Robust issues in estimating modes for multivariate torus data (p.21)
Claudio Agostinelli, Giovanni Saraceno and Luca Greco
Robust estimation of parsimonious finite mixture of Gaussian models (p.92)
Luis Angel García-Escudero, Agustín Mayo-Iscar and Marco Riani
Robust supervised clustering: some practical issues (p.142)
Fabrizio Laurini and Gianluca Morelli
INV 1.4
Flexible Bayesian mixture models for complex data
Organizer and Chair: Alessandra Guglielmi
Prediction of large observations via Bayesian inference for extreme-value theory (p.231)
Isadora Antoniano Villalobos, Simone Padoan and Boris Beranger
Model-based clustering for categorical data via Hamming distance (p.31)
Raffaele Argiento, Edoardo Filippi-Mazzola and Lucia Paci
MCMC computations for Bayesian mixture models using repulsive point processes (p.167)
Jesper Møller, Mario Beraha, Raffaele Argiento and Alessandra Guglielmi
12:30 – 13:30
KEY #1
Keynote #1 – Optimal transport methods for fairness in machine learning
Jean-Michel Loubes, Université Toulouse Paul Sabatier (FRANCE)
Chair: Francesca Grieselin
CLADAG 2021 Book of Abstracts and Short papers, p. 5
13:30 – 14:00
Lunch break
14:00 – 15:00
Contributed sessions #1
CON 1.A
Models for clustering
Exploring solutions via monitoring for cluster weighted robust models (p.284)
Andrea Cappozzo, Luis Angel Garcìa Escudero, Francesca Greselin and Agustìn Mayo-Iscar
Clustering production indexes for construction with forecast distributions (p.360)
Michele La Rocca, Francesco Giordano and Cira Perna
Semi-constrained model-based clustering of mixed-type data using a composite likelihood approach (p.408)
Roberto Rocci and Monia Ranalli
Clustering models for three-way data (p.432)
Donatella Vicari and Paolo Giordani
CON 1.B
Nonparametric and semiparametric methods
Semiparametric finite mixture of regression models with Bayesian P-splines (p.268)
Marco Berrettini, Giuliano Galimberti and Saverio Ranciati
Angular halfspace depth: classification using spherical bagdistances (p.316)
Houyem Demni, Davide Buttarazzi, Stanislav Nagy and Giovanni Camillo Porzio
Functional cluster analysis of HDI evolution in European countries (p.336)
Francesca Fortuna, Alessia Naccarato and Silvia Terzi
A nonparametric test for mode significance (p.388)
Giovanna Menardi and Federico Ferraccioli
CON 1.C
Data analysis in biomedical science
A subject-specific measure of interrater agreement based on the homogeneity index (p.272)
Giuseppe Bove
Clustering longitudinal data with category theory for diabetic kidney disease (p.364)
Maria Mannone, Veronica Distefano, Claudio Silvestri and Irene Poli
Antibodies to SARS-CoV-2: an exploratory analysis carried out through the Bayesian profile regression (p.412)
Annalina Sarra, Adelia Evangelista, Tonio Di Battista and Damiana Pieragostino
Modelling three-way RNA sequencing data with mixture of multivariate Poisson-lognormal distribution (p.416)
Theresa Scharl and Bettina Grün
CON 1.D
Modelling dependence structures
An alternative to joint graphical lasso for learning multiple Gaussian graphical models (p.332)
Lorenzo Focardi Olmi and Anna Gottard
A Bayesian framework for structural learning of mixed graphical models (p.344)
Chiara Galimberti, Federico Castelletti and Stefano Peluso
Model selection procedure for mixture hidden Markov models (p.243)
Antonino Abbruzzo, Maria Francesca Cracolici and Furio Urso
CON 1.E
Data analysis in economics and finance
Predictive power of Bayesian CAR models on scale free networks: an application for credit risk (p.264)
Claudia Berloco, Raffaele Argiento and Silvia Montagna
A Machine Learning Approach in stock risk management (p.308)
Salvatore Cuomo, Federico Gatta, Fabio Giampaolo, Carmela Iorio and Francesco Piccialli
Clustering income data based on share densities (p.300)
Francesca Condino
Pathmox segmentation trees to compare linear regression models (p.312)
Cristina Davino and Giuseppe Lamberti
15:15 – 16:30
Invited sessions #2
INV 2.1
Copulas in time series analysis
Organizer and Chair: Marta L. Di Lascio, Roberta Pappadà
DTW-based assessment of the predictive power of the copula-DCC-GARCH-MST model developed for European insurance institutions (p.71)
Anna Denkowska and Stanisław Wanat
Nonlinear Interconnectedness of crude oil and financial markets (p.173)
Yarema Okhrin, Gazi Salah Uddin and Muhammad Yahya
Assessing food security issues in Italy: a quantile copula approach (p.195)
Giorgia Rivieccio, Jean-Paul Chavas, Giovanni De Luca, Salvatore Di Falco and Fabian Capitanio
INV 2.2
Advances in mixture models for matrix-variate and tensor data
Organizer and Chair: Antonio Punzo
Using Subset Log-Likelihoods to Trim Outliers in Gaussian Mixture Models
Katharine Clark and Paul McNicholas
Tensor-variate finite mixture model for the analysis of university professor remuneration (p.208)
Shuchismita Sarkar, Volodymyr Melnykov and Xuwen Zhu
Network analysis implementing a mixture distribution from Bayesian viewpoint (p.210)
Jarod Smith, Mohammad Arashi and Andriette Bekker
INV 2.3
Methods for inference from innovative or multiple data sources
Organizer and Chair: Emilia Rocco, Chiara Bocci
Improving the reliability of a nonprobability web survey (p.120)
Yinxuan Huang and Natalie Shlomo
A nonparametric approach for statistical matching under informative sampling and nonresponse (p.146)
Daniela Marella and Danny Pfeffermann
Measurement errors in multiple systems estimation (p.211)
Paul Smith, Peter van der Heijden and Maarten Cruyff
INV 2.4
Social inequalities
Organizer and Chair: Mariangela Zenga
Socioeconomic inequalities and cancer risk: myth or reality? (p.106)
Carlotta Galeone
Quantifying the impact of covariates on the gender gap measurement: an analysis based on EU-SILC data from Poland and Italy (p.108)
Francesca Greselin and Alina Jędrzejczak
Gender inequalities from an income perspective (p.158)
Marcella Mazzoleni, Angiola Pollastri and Vanda Tulli
16:45 – 17:45
KEY #2
Keynote #2 – Veridical Data Science: the practice of responsible data analysis and decision-making
Bin Yu, University of California Berkeley (USA)
Chair: Maurizio Vichi
CLADAG 2021 Additional Abstracts
18:00 – 19:15
Invited sessions #3
INV 3.1
Recent advances in item response theory models
Organizer and Chair: Silvia Cagnone
Investigating model fit in item response models with the Hellinger distance (p.150)
Mariagiulia Matteucci and Stefania Mignani
Boosting multidimensional IRT models (p.40)
Michela Battauz and Paolo Vidoni
A study of lack-of-fit diagnostics for models fit to cross-classified binary variables (p.191)
Mark Reiser and Maduranga Dassanayake
INV 3.2
Advances in clustering
Organizer and Chair: Luca Frigau
Cluster validity by random forests (p.91)
Tahir Ekin and Claudio Conversano
Non-parametric consistency for the Gaussian mixture maximum likelihood estimator (p.116)
Christian Hennig and Pietro Coretto
Minimizing conflicts of interest: optimizing the JSM program (p.240)
Qiuyi Wu and David Banks
INV 3.3
Advances in parsimonious mixture modeling
Organizer: Volodymyr Melnykov
Chair: Xuwen Zhu
Parameter-wise co-clustering for high dimensional data (p.107)
Michael Gallaugher, Christophe Biernacki and Paul McNicholas
Transformation mixture modeling for skewed data groups with heavy tails and scatter (p.162)
Yana Melnykov, Xuwen Zhu and Volodymyr Melnykov
Clustering via new parsimonious mixtures of heavy tailed distributions (p.216)
Salvatore Daniele Tomarchio, Luca Bagnato and Antonio Punzo
INV 3.4
Bayesian non parametrics methods for classification
Organizer and Chair: Bruno Scarpa
Bayesian nonparametric dynamic modeling of psychological traits (p.25)
Emanuele Aliverti
Semiparametric IRT models for non-normal latent traits (p.178)
Sally Paganin
Malaria risk detection via mixed membership models (p.203)
Massimiliano Russo
Day 2 – Friday, 10 Sept 2021
9:15 - 10:30
CLADAG ASSEMBLY
Cladag assembly
10:45 – 12:00
Invited sessions #4
INV 4.1
Recent developments in symbolic data analysis
Organizer and Chair: Paula Brito
A generalised clusterwise regression for distributional data (p.223)
Rosanna Verde, Francisco T. de A. De Carvalho and Antonio Balzanella
Detection of internet attacks with histogram principal component analysis (p.174)
M. Rosário Oliveira, Ana Subtil and Lina Oliveira
Identifying mortality patterns of main causes of death among young EU population using SDA approaches (p.141)
Simona Korenjak-Černe and Nataša Kejžar
INV 4.2
Recent advances in dynamic clustering: Markov models and extensions
Organizer and Chair: Daniele Tomarchio
Accounting for response behavior in longitudinal rating data (p.58)
Roberto Colombi, Sabrina Giordano and Maria Kateri
Modeling clusters of corporate defaults: regime-switching models significantly reduce the contagion source
Bård Støve, Geir D. Berentsen, Jan Bulla and Antonello Maruotti
Two-step estimation of multilevel latent class models with covariates (p.75)
Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser and Jouni Kuha
INV 4.3
Networks data analysis and applications
Organizer and Chair: Mario R. Guarracino
Sender and receiver effects in latent space models for multiplex data (p.68)
Silvia D’Angelo
Networks of networks (p.186)
Panos Pardalos
Community detection in tripartite networks of university students mobility flows (p.232)
Maria Prosperina Vitale, Vincenzo Giuseppe Genova, Giuseppe Giordano and Giancarlo Ragozini
INV 4.4
Recent developments in the statistical analysis of categorical data
Organizer and Chair: Claudia Tarantola
A risk indicator for categorical data (p.93)
Silvia Facchinetti and Silvia Angela Osmetti
A semi-Bayesian approach for the analysis of scale effects in ordinal regression models (p.124)
Maria Iannario and Claudia Tarantola
Simple effect measures for interpreting generalized binary regression models (p.129)
Maria Kateri
12:15 – 13:30
Invited sessions #5
INV 5.1
New issues in univariate and multivariate quantile regression
Organizer and Chair: Lea Petrella
Understanding and estimating conditional parametric quantile models (p.44)
Matteo Bottai
On model-based clustering using quantile regression (p.102)
Carlo Gaetan, Paolo Girardi and Victor Muthama Musau
Unconditional M-quantile regression (p.163)
Luca Merlo, Lea Petrella and Nikos Tzavidis
INV 5.2
Penalized techniques for data analysis
Organizer and Chair: Gianluca Sottile
Shapley Lorenz methods for eXplainable artificial intelligence (p.45)
Niklas Bussmann, Roman Enzmann, Paolo Giudici and Emanuela Raffinetti
Smoothed non linear PCA for multivariate data (p.54)
Marcello Chiodi
Causal regularization (p.236)
Ernst Wit and Lucas Kania
INV 5.3
Latent variable mixture modeling in epidemiology
Organizers: Maria Iannario
Characterising longitudinal trajectories of COVID-19 biomarkers within a latent class framework (p.64)
Federica Cugnata, Chiara Brombin, Pietro Cippà, Alessandro Ceschi, Paolo Ferrari and Clelia Di Serio
Pairwise likelihood estimation of latent autoregressive count models (p.187)
Xanthi Pedeli and Cristiano Varin
A machine learning approach for evaluating anxiety in neurosurgical patients during the COVID-19 pandemic (p.227)
Marika Vezzoli, Francesco Doglietto, Stefano Renzetti, Marco Fontanella and Stefano Calza
INV 5.4
Co-clustering for temporal sequences and distributional data
Organizer and Chair: Rosanna Verde
Mining multiple time sequences through co-clustering algorithms for distributional data (p.32)
Antonio Balzanella, Antonio Irpino and Francisco de A.T. De Carvalho
Co-clustering for high dimensional sparse data
Nicoleta Rogovschi
A general bi-clustering technique for functional data (p.217)
Agostino Torti, Marta Galvani, Alessandra Menafoglio, Piercesare Secchi and Simone Vantini
13:30 – 14:00
Lunch break
14:00 – 15:15
Invited sessions #6
INV 6.1
Advances in robust cluster analysis
Organizers: Luis Angel García-Escudero, Agustín Mayo-Iscar, Marco Riani
Chair: Agustín Mayo-Iscar
Robust classification of spectroscopic data in agri-food: first analysis on the stability of results (p.49)
Andrea Cappozzo, Ludovic Duponchel, Francesca Greselin and Brendan Murphy
Robust classification in high dimensions using regularized covariance estimates (p.215)
Valentin Todorov and Peter Filzmoser
Issues in monitoring the EU trade of critical COVID-19 commodities(p.53)
Andrea Cerasa, Enrico Checchi, Domenico Perrotta and Francesca Torti
INV 6.2
Bayesian analysis of finite and infinite mixtures
Organizer and Chair: Bettina Grün
PIVMET: pivotal methods for Bayesian relabelling in finite mixture models (p.87)
Leonardo Egidi, Roberta Pappadà, Francesco Pauli and Nicola Torelli
A transdimensional MCMC sampler for spatially dependent mixture models (p.112)
Alessandra Guglielmi, Mario Beraha, Matteo Giannella, Matteo Pegoraro and Riccardo Peli
Infinite mixtures of infinite factor analysers (p.168)
Keefe Murphy, Cinzia Viroli and Isobel Claire Gormley
INV 6.3
Latent variable models for constructing composite indices
Organizers and Chairs: Rosaria Romano
PCA-based composite indices and measurement model (p.154)
Matteo Mazziotta and Adriano Pareto
Specifying composites in structural equation modeling: the Henseler-Ogasawara specification (p.209)
Florian Schuberth
Developing a multidimensional and hierarchical index following a composite-based approach (p.220)
Laura Trinchera
INV 6.4
Issues in directional data analysis
Organizer and Chair: Giovanni Camillo Porzio
Best approach direction for spherical random variables (p.128)
Jayant Jha
Angular halfspace depth: computation (p.169)
Stanislav Nagy, Petra Laketa and Rainer Dyckerhoff
Nonparametric estimation of the number of clusters for directional data (p.207)
Paula Saavedra-Nieves and Rosa M. Crujeiras
15:30 – 16:30
KEY #3
Keynote #3 – Understanding cross-validation and prediction error
Robert Tibshirani, Stephen Bates and Trevor Hastie
Chair: Anna Gottard
CLADAG 2021 Book of Abstracts and Short papers, p. 7
16:45 – 17:45
KEY #4
Keynote #4 – Class maps for visualizing classification results
Peter Rousseeuw, Jakob Raymaekers and Mia Hubert
Chair: Marco Riani
CLADAG 2021 Book of Abstracts and Short papers, p. 6
18:00 - 20:00
PLENARY
Plenary – Statistical Issues in the COVID-19 Pandemic
Organizer and Chair: J. Sunil Rao
A simple correction for COVID-19 sampling bias
Daniel Diaz
CLADAG 2021 Book of Abstracts and Short papers, p. 14
A seat at the table: the key role of biostatistics and data science in the COVID-19 pandemic
Jeffrey S. Morris
CLADAG 2021 Book of Abstracts and Short papers, p. 15
Predictions, role of interventions and the crisis of virus in India: a data science call to arms
Bhramar Mukherjee
CLADAG 2021 Book of Abstracts and Short papers, p. 16
Bhramar Mukherjee
CLADAG 2021 Book of Abstract and Short papers, p. 16
Contributions of Israel’s CBS to rout COVID-19
Danny Pfeffermann
CLADAG 2021 Book of Abstracts and Short papers, p. 17
Day 3 – Saturday, 11 Sept 2021
10:00 – 11:15
Invited sessions #7 & Contributed sessions #2
INV 7.1
Recent advances in directional statistics
Organizer and Chair: Stefania Fensore, Agnese Panzera
Mixtures of Kato–Jones distributions on the circle, with an application to traffic count data (p.133)
Shogo Kato, Kota Nagasaki and Wataru Nakanishi
How to design a directional distribution (p.137)
John Kent
A graphical depth-based aid to detect deviation from unimodality on hyperspheres (p.182)
Giuseppe Pandolfo
INV 7.2
Recent developments in flexible regression – methods and software
Organizer and Chair: Marco Geraci
Additive quantile regression via the qgam R package (p.97)
Matteo Fasiolo
Additive Bayesian variable selection under censoring and misspecification
Javier Rubio
CON 2.A
Robust methods and data diagnostic
A combined test of the Benford hypothesis with anti-fraud applications (p.256)
Lucio Barabesi, Andrea Cerasa, Andrea Cerioli and Domenico Perrotta
Multivariate outlier detection for histogram-valued variables (p.384)
Ana Martins, Paula Brito, Sónia Dias and Peter Filzmoser
Angular halfspace depth: Central regions (p.356)
Petra Laketa and Stanislav Nagy
Robustness methods for modelling count data with general dependence structures (p.396)
Marta Nai Ruscone and Dimitris Karlis
A robust quantile approach to ordinal trees (p.424)
Rosaria Simone, Cristina Davino, Domenico Vistocco and Gerhard Tutz
CON 2.B
Web mining and textual data analysis
Unabalanced classfication of electronic invoicing (p.260)
Chiara Bardelli
The use of multiple imputation techniques for social media data (p.372)
Paolo Mariani, Andrea Marletta and Matteo Locci
Visualizing cluster of words: a graphical approach to grammar acquisition (p.392)
Massimo Mucciardi, Giovanni Pirrotta, Andrea Briglia and Arnaud Sallaberry
Using eye-traking data to create a weighted dictionary for sentiment analysis: the eye dictionary (p.436)
Gianpaolo Zammarchi and Jaromir Antoch
11:30 – 12:30
Contributed sessions #3
CON 3.A
Data analysis in biology and environmental sciences
Model-based clustering for estimating cetaceans site-fidelity and abundance (p.292)
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
Prediction of gene expression from transcription factors affinities: an application of Bayesian non-linear modelling (p.376)
Federico Marotta, Paolo Provero and Silvia Montagna
High dimensional model-based clustering of European georeferenced vegetation plots (p.380)
Francesca Martella, Fabio Attorre, Michele De Sanctis and Giuliano Fanelli
Bayesian analysis of a water quality high-frequency time series through Markov switching autoregressive models (p.400)
Roberta Paroli, Luigi Spezia, Marc Stutter and Andy Vinten
CON 3.B
Process and service quality evaluation
Estimating latent linear correlations from fuzzy contingency tables (p.276)
Antonio Calcagnì
The LP data depth and its application to multivariate process control charts (p.352)
Carmela Iorio, Giuseppe Pandolfo, Michele Staiano, Massimo Aria and Roberta Siciliano
Detecting the effect of secondary school in higher education university choices (p.404)
Mariano Porcu, Isabella Sulis and Cristian Usala
CON 3.C
Mixture models and random effect models
Estimating Bayesian mixtures of finite mixtures with telescoping sampling (p.340)
Sylvia Frühwirth-Schnatter, Bettina Grün and Gertraud Malsiner-Walli
Measurement error models on spatial network lattices: car crashes in Leeds (p.348)
Andrea Gilardi, Riccardo Borgoni, Luca Presicce and Jorge Mateu
A redundancy analysis with multivariate random-coefficients linear models (p.368)
Laura Marcis, Maria Chiara Pagliarella and Renato Salvatore
Group-dependent finite mixture model (p.304)
Paula Costa Fontichiari, Miriam Giuliani, Raffaele Argiento and Lucia Paci
CON 3.D
Machine learning and statistical learning
Semi-supervised Learning through depth functions (p.255)
Simona Balzano, Mario Rosario Guarracino and Giovanni Camillo Porzio
Model-based clustering with sparse matrix mixture models (p.280)
Andrea Cappozzo, Alessandro Casa and Michael Fop
Neural networks for high cardinality categorical data (p.320)
Agostino Di Ciaccio
Stacking ensemble of Gaussian mixtures (p.420)
Luca Scrucca
CON 3.E
Hierarchical clustering and classification methods
A full mixture of experts model to classify constrained data (p.247)
Roberto Ascari and Sonia Migliorati
Categorical classifiers in multi-class classification problems (p.288)
Maurizio Carpita and Silvia Golia
Ali-Mikhail-Haq copula to detect low correlations in hierarchical clustering (p.324)
F. Marta L. Di Lascio, Andrea Menapace and Roberta Pappadà
Model-based clustering with parsimonious covariance structure (p.296)
Carlo Cavicchia, Maurizio Vichi and Giorgia Zaccaria
12:45 – 13:45
KEY #5
Keynote #5 – Quantile-based classification
Cinzia Viroli, University of Bologna (ITALY)
Chair: Brendan Murphy
CLADAG 2021 Book of Abstracts and Short papers, p. 8