A semiparametric approach to mixed outcome latent variable models: Estimating the association between cognition and regional brain volumes
Abstract
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.
Keywords
Cite
@article{arxiv.1401.2728,
title = {A semiparametric approach to mixed outcome latent variable models: Estimating the association between cognition and regional brain volumes},
author = {Jonathan Gruhl and Elena A. Erosheva and Paul K. Crane},
journal= {arXiv preprint arXiv:1401.2728},
year = {2014}
}
Comments
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)