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December 2013 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
Ann. Appl. Stat. 7(4): 2361-2383 (December 2013). DOI: 10.1214/13-AOAS675

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.

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Jonathan Gruhl. Elena A. Erosheva. Paul K. Crane. "A semiparametric approach to mixed outcome latent variable models: Estimating the association between cognition and regional brain volumes." Ann. Appl. Stat. 7 (4) 2361 - 2383, December 2013. https://doi.org/10.1214/13-AOAS675

Information

Published: December 2013
First available in Project Euclid: 23 December 2013

zbMATH: 1283.62218
MathSciNet: MR3161726
Digital Object Identifier: 10.1214/13-AOAS675

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 4 • December 2013
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