A SEMIPARAMETRIC APPROACH TO MIXED OUTCOME LATENT VARIABLE MODELS: ESTIMATING THE ASSOCIATION BETWEEN COGNITION AND REGIONAL BRAIN VOLUMES

• 成果类型: Article
• 署名作者: Gruhl, Jonathan; Erosheva, Elena A.; Crane, Paul K.
• 署名单位: University of Washington; University of Washington Seattle; Harborview Medical Center; University of Washington; University of Washington Seattle
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/13-AOAS675
• 发表日期: 2013
• 页码: 2361-2383
• 关键词: parameter expansion; likelihood; constraints; uniqueness; inference; em

摘要

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.