Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data

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 incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying, estimating, and interpreting the parameters. In this article, we propose a new class of Dimension-Grouped MMMs (Gro-M$^3$s) for multivariate categorical data, which improve parsimony and interpretability. In Gro-M$^3$s, observed variables are partitioned into groups such that the latent membership is constant for variables within a group but can differ across groups. Traditional latent class models are obtained when all variables are in one group, while traditional MMMs are obtained when each variable is in its own group. The new model corresponds to a novel decomposition of probability tensors. Theoretically, we derive transparent identifiability conditions for both the unknown grouping structure and model parameters in general settings. Methodologically, we propose a Bayesian approach for Dirichlet Gro-M$^3$s to inferring the variable grouping structure and estimating model parameters. Simulation results demonstrate good computational performance and empirically confirm the identifiability results. We illustrate the new methodology through applications to a functional disability survey dataset and a personality test dataset

Gender-based homophily in collaborations across a heterogeneous scholarly landscape

Using the corpus of JSTOR articles, we investigate the role of gender in collaboration patterns across the scholarly landscape by analyzing gender-based homophily--the tendency for researchers to co-author with individuals of the same gender. For a nuanced analysis of gender homophily, we develop methodology necessitated by the fact that the data comprises heterogeneous sub-disciplines and that not all authorships are exchangeable. In particular, we distinguish three components of gender homophily in collaborations: a structural component that is due to demographics and non-gendered authorship norms of a scholarly community, a compositional component which is driven by varying gender representation across sub-disciplines, and a behavioral component which we define as the remainder of observed homophily after its structural and compositional components have been taken into account. Using minimal modeling assumptions, we measure and test for behavioral homophily. We find that significant behavioral homophily can be detected across the JSTOR corpus and show that this finding is robust to missing gender indicators in our data. In a secondary analysis, we show that the proportion of female representation in a field is positively associated with significant behavioral homophily

The Physical and Mental Health of Lesbian, Gay Male, and Bisexual (LGB) Older Adults: The Role of Key Health Indicators and Risk and Protective Factors

Purpose: Based on resilience theory, this paper investigates the influence of key health indicators and risk and protective factors on health outcomes (including general health, disability, and depression) among lesbian, gay male, and bisexual (LGB) older adults. Design and Methods: A cross-sectional survey was conducted with LGB older adults, aged 50 and older (N = 2,439). Logistic regressions were conducted to examine the contributions of key health indicators (access to health care and health behaviors), risk factors (lifetime victimization, internalized stigma, and sexual identity concealment), and protective factors (social support and social network size) to health outcomes, when controlling for background characteristics. Results: The findings revealed that lifetime victimization, financial barriers to health care, obesity, and limited physical activity independently and significantly accounted for poor general health, disability, and depression among LGB older adults. Internalized stigma was also a significant predictor of disability and depression. Social support and social network size served as protective factors, decreasing the odds of poor general health, disability, and depression. Some distinct differences by gender and sexual orientation were also observed. Implications: High levels of poor general health, disability, and depression among LGB older adults are of major concern. These findings highlight the important role of key risk and protective factors, which significantly influences health outcomes among LGB older adults. Tailored interventions must be developed to address the distinct health issues facing this historically disadvantaged population

Physical and Mental Health of Transgender Older Adults: An At-Risk and Underserved Population

Purpose: This study is one of the first to examine the physical and mental health of transgender older adults and to identify modifiable factors that account for health risks in this underserved population. Design and Methods: Utilizing data from a cross-sectional survey of lesbian, gay, bisexual, and transgender older adults aged 50 and older (N = 2,560), we assessed direct and indirect effects of gender identity on 4 health outcomes (physical health, disability, depressive symptomatology, and perceived stress) based on a resilience conceptual framework. Results: Transgender older adults were at significantly higher risk of poor physical health, disability, depressive symptomatology, and perceived stress compared with nontransgender participants. We found significant indirect effects of gender identity on the health outcomes via fear of accessing health services, lack of physical activity, internalized stigma, victimization, and lack of social support; other mediators included obesity for physical health and disability, identity concealment for perceived stress, and community belonging for depressive symptomatology and perceived stress. Further analyses revealed that risk factors (victimization and stigma) explained the highest proportion of the total effect of gender identity on health outcomes. Implications: The study identifies important modifiable factors (stigma, victimization, health-related behaviors, and social support) associated with health among transgender older adults. Reducing stigma and victimization and including gender identity in nondiscrimination and hate crime statutes are important steps to reduce health risks. Attention to bolstering individual and community-level social support must be considered when developing tailored interventions to address transgender older adults’ distinct health and aging needs

Will the market bear your asking price? 1

The current practice for determining market price of a real estate often relies on selecting a list of comparable sales and does not employ modern statistical techniques. The example in this paper demonstrates how one could use statistical modeling in estimating the market value of a real estate. This paper illustrates a real estate market analysis via a statistical modeling case study which allows one not only to estimate the market value of a real estate but also to learn some characteristics of the market. The statistical analysis scheme presented fits naturally in the common practice used by real estate agents as it relies on identified comparable sales as the starting point.

Bayesian mixed membership models for soft clustering and classification

work was presented in a plenary lecture at the 28th Annual Conference of the German Classification Society, Dortmund. We are indebted to John Lafferty for his collaboration on the analysis of the PNAS data which we report here, and to Adrian Raftery and Christian Robert for helpful discussions on selecting K. Erosheva’s work was supported by NIH grants 1 RO1 AG023141-01 and R01 CA94212-01, Fienberg’s work was supported by NIH grant 1 RO1 AG023141-01 and by the Centre de Recherche en Economie et Statistique of the Institut National de la Statistique e