167 citations · 226 across the 12 of their papers we have counts for
14 papers
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…
Identifiability of Partial-Mastery Cognitive Diagnostic Models
Jun Wu, Patrícia Martinková, Elena Erosheva
Partial-mastery (PM) cognitive diagnostic models (CDMs) extend traditional CDMs by replacing binary latent attribute mastery indicators with continuous mastery scores for multiple…
Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings
Michael Pearce, Elena A. Erosheva
Rankings and ratings are commonly used to express preferences but provide distinct and complementary information. Rankings give ordinal and scale-free comparisons but lack granular…
Partial-Mastery Cognitive Diagnosis Models
Zhuoran Shang, Elena A. Erosheva, Gongjun Xu
Cognitive diagnosis models (CDMs) are a family of discrete latent attribute models that serve as statistical basis in educational and psychological cognitive diagnosis assessments.…
On the validity of bootstrap uncertainty estimates in the Mallows-Binomial model
Michael Pearce, Elena A. Erosheva
The Mallows-Binomial distribution is the first joint statistical model for rankings and ratings (Pearce and Erosheva, 2022). Because frequentist estimation of the model parameters…
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Michael Pearce, Elena A. Erosheva
Rankings and scores are two common data types used by judges to express preferences and/or perceptions of quality in a collection of objects. Numerous models exist to study data of…
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
Yuqi Gu, Elena A. Erosheva, Gongjun Xu +1
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs in…
Co-clustering of time-dependent data via Shape Invariant Model
Alessandro Casa, Charles Bouveyron, Elena Erosheva +1
Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these d…
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…
On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example
Yen-Chi Chen, Y. Samuel Wang, Elena A. Erosheva
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning. It has been a…
On the relationship between set-based and network-based measures of gender homophily in scholarly publications
Y. Samuel Wang, Elena A. Erosheva
There is an increased interest in the scientific community in the problem of measuring gender homophily in co-authorship on scholarly publications (Eisen, 2016). For a given set of…
A Variational EM Method for Mixed Membership Models with Multivariate Rank Data: an Analysis of Public Policy Preferences
Y. Samuel Wang, Ross Matsueda, Elena A. Erosheva
In this article, we consider modeling ranked responses from a heterogeneous population. Specifically, we analyze data from the Eurobarometer 34.1 survey regarding public policy pre…
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…
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…