2013/12/01 by Jonathan Gruhl, Elena A. Erosheva, Paul K. Crane · 15 citations
Computer Science · Decision Sciences · Mathematics · #Bayesian Methods and Mixture Models #Bayesian probability #Cognition #Latent variable #Latent variable model #Markov chain Monte Carlo #Multivariate statistics #Psychometric Methodologies and Testing #Semiparametric model #Semiparametric regression #Statistical Methods and Bayesian Inference #stat.AP
paper · pdf · doi:10.1214/13-aoas675
published in The Annals of Applied Statistics 7(4) (Institute of Mathematical Statistics) · Published in at http://dx.doi.org/10.1214/13-AOAS675 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2013/12/01 · arxiv created 2014/01/13 · arxiv updated 2014/01/14 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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 model for multivariate data of arbitrary type that does not require specification of conditional distributions. Drawing on the extended rank likelihood method by Hoff [Ann. Appl. Stat. 1 (2007) 265–283], we develop a semiparametric approach for latent variable modeling with mixed outcomes and propose associated Markov chain Monte Carlo estimation methods. Motivated by cognitive testing data, we focus on bifactor models, a special case of factor analysis. We employ our semiparametric Bayesian latent variable model to investigate the association between cognitive outcomes and MRI-measured regional brain volumes.