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InterConnect webinar: Daniel Kowal
October 13 @ 16:00 - 17:30
On-site and online seminar: Friday 13th October 2023 from 4.00 – 5.00 PM
Title: Nonparametric Copula Models for Multivariate, Mixed, and Missing Data
Speaker: Daniel Kowal (Rice University)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
Modern datasets commonly feature both substantial missingness and many variables of mixed data types, which present significant challenges for estimation and inference. Complete case analysis, which proceeds using only the observations with fully-observed variables, is often severely biased, while model-based imputation of missing values is limited by the ability of the model to capture complex dependencies among (possibly many) variables of mixed data types. To address these challenges, we develop a novel Bayesian mixture copula for joint and nonparametric modelling of multivariate count, continuous, ordinal, and unordered categorical variables, and deploy this model for inference, prediction, and imputation of missing data. Most uniquely, we introduce a new and computationally efficient strategy for marginal distribution estimation that eliminates the need to specify any marginal models yet delivers posterior consistency for each marginal distribution and the copula parameters under missingness-at-random. Extensive simulation studies demonstrate exceptional modelling and imputation capabilities relative to competing methods, especially with mixed data types, complex missingness mechanisms, and nonlinear dependencies. We conclude with a data analysis that highlights how improper treatment of missing data can distort a statistical analysis, and how the proposed approach offers a resolution.
Dr. Dan Kowal is the Dobelman Family Assistant Professor in the Department of Statistics at Rice University. His research interests include Bayesian models and algorithms for large and dependent data, decision analysis for interpretable and actionable model summarization, and synthesis and imputation of mixed data. Application areas include public health, epidemiology and environmental justice, physical activity data, economics, and finance. Dr. Kowal’s research has been recognized with a Young Investigator Award from the Army Research Office, the inaugural Blackwell-Rosenbluth Award, and multiple paper and presentation awards. He received his PhD from Cornell University.