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20072026
most citedDescribing disability through individual-level mixture models for multivariate binary data

167 citations · 226 across the 12 of their papers we have counts for

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stat.AP2026

Can statistical models capture Mamdani's success? Social choice, ranked-choice voting, and model fit, with an application to the 2025 New York City Democratic Primary

Michael Pearce, Erin R. Lipman, Christopher Adolph +1

Ranked-choice voting is increasingly prevalent in elections. An extensive literature in social choice theory -- the study of collective decision-making with the goal of making comp…

stat.AP2019★ 7 cited

Gender-based homophily in collaborations across a heterogeneous scholarly landscape

Y. Samuel Wang, Carole J. Lee, Jevin D. West +2

In this article, we investigate the role of gender in collaboration patterns by analyzing gender-based homophily -- the tendency for researchers to co-author with individuals of th…

stat.AP2014★ 15 cited

A semiparametric approach to mixed outcome latent variable models: Estimating the association between cognition and regional brain volumes

Jonathan Gruhl, Elena A. Erosheva, Paul K. Crane

Multivariate data that combine binary, categorical, count and continuous outcomes are common in the social and health sciences. We propose a semiparametric Bayesian latent variable…

stat.AP2007★ 167 cited

Describing disability through individual-level mixture models for multivariate binary data

Elena A. Erosheva, Stephen E. Fienberg, Cyrille Joutard

Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population…