期刊
ANNALS OF APPLIED STATISTICS
卷 7, 期 4, 页码 2361-2383出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/13-AOAS675
关键词
Latent variable model; Bayesian hierarchical model; extended rank likelihood; cognitive outcomes
资金
- National Institute on Aging [R01 AG 029672]
- [P01 AG12435]
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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