Elena A. Erosheva (she/her)

Graduate Program Coordinator, University of Washington


Email erosheva@uw.edu
Phone +1 206 685-0166
UW Box Number 354320
Homepage Personal Home Page 
ORCID iD  0000-0003-2162-0017 

Joint: CSSS and School of Social Work
Research: Statistical methodology for the social, behavioral, and health sciences, Bayesian inference, discrete data analysis, hierarchical and latent variables models.

Bio:
Elena A. Erosheva is a Professor of Statistics and Social Work and the Associate Director of the Center for Statistics and the Social Sciences at the University of Washington. Her research focuses on the development and application of statistical methods and models for complex and heterogeneous data in the social, behavioral, medical and health sciences. She is a recipient of the 2013 Mitchell Prize from the International Society of Bayesian Analysis for an outstanding paper that describes how a Bayesian analysis has solved an important applied problem, and a first prize winner of 2014 America Competes Act Challenge competition to maximize fairness in NIH peer review in the category Most Creative Idea for Detection of Bias in Peer Review. Erosheva is currently serving as an ArXiv moderator for Statistics, and her current and past editorial board service includes Annals of Applied Statistics, JASA, Journal of Educational and Behavioral Statistics, and Psychometrika. 

Preprints

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…

Gender-based homophily in collaborations across a heterogeneous scholarly landscape
Y. Samuel Wang, Carole J. Lee, Jevin D. West, Carl T. Bergstrom, Elena A. Erosheva
In this article, we investigate the role of gender in collaboration patterns by analyzing gender-based homophily -- the tendency for researchers to co-author…

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
Yuqi Gu, Elena A. Erosheva, Gongjun Xu, David B. Dunson
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…

Co-clustering of time-dependent data via Shape Invariant Model
Alessandro Casa, Charles Bouveyron, Elena Erosheva, Giovanna Menardi
Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application…

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…

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…

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…

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…

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…