[Statlist] Research Webinar in Statistics *FRIDAY 8 APRIL 2022* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organized by Professor Sebastian Engelke on behalf of the Research Center for Statistics (https://www.unige.ch/gsem/en/research/institutes/rcs/)


FRIDAY 8 APRIL 2022 at 11:15am
ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192


GEE-Assisted Variable Selection for Binary Latent Variable Models
(jointly with Samuel Mueller, School of Mathematics and Physical Sciences, Macquarie University, and A.H. Welsh, Research School of Finance, Actuarial Studies and Statistics, The Australian National University)
Francis K.C. HUI, The Australian National University, Australia
https://researchers.anu.edu.au/researchers/hui-fkc

ABSTRACT:
Multivariate data are commonly analyzed using one of two approaches: a conditional approach based on generalized linear latent variable models (GLLVMs) or some variation thereof, and a marginal approach based on generalized estimating equations (GEEs). With research on mixed models and GEEs having gone down separate paths, there is a common mindset to treat the two approaches as mutually exclusive, with which to use driven by the question of interest. Focusing on multivariate binary responses, in this talk we study the connections between the parameters from conditional and marginal models, with the aim of using GEEs for fast variable selection in GLLVMs. We accomplish this through two main advances. First, we show that GEEs are zero consistent for GLLVMs fitted to multivariate binary data. That is, if the true model is a GLLVM but we misspecify and fit GEEs, then the latter is able to asymptotically differentiate between truly zero versus non-zero coefficients in the former. Building on this result, we propose GEE-assisted variable selection for GLLVMs using score- and Wald-based information criteria to construct a fast forward selection path followed by pruning. We demonstrate GEE-assisted variable selection is selection consistent for the underlying GLLVM, with simulation studies demonstrating its strong finite sample performance and computational efficiency.

BIOGRAPHY:
Francis Hui is a senior lecturer in statistics and Australian Research Council DECRA fellow at The Australian National University. He completed his PhD at the University of New South Wales in 2014 and moved to Canberra shortly afterwards to undertake a postdoctoral fellowship at the ANU, where he has been happily stuck there ever since. He was the recipient of the Australia Academy of Science Christopher Heyde Medal (2021). His research spans a mixture of methodological, computational, and applied statistics, including longitudinal and correlated data analysis, dimension reduction and variable selection, and approximate statistical estimation and inference. Much of his research is motivated by joint modeling in ecology and longitudinal analysis of mental health data, complemented by copious amounts of tea drinking and unhealthy amounts of anime watching.


Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/