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

作者：Gruhl Jonathan^*; Erosheva Elena A; Crane Paul K

来源：Annals of Applied Statistics, 2013, 7(4): 2361-2383.

DOI：10.1214/13-AOAS675

• Latent variable model
• Bayesian hierarchical model
• extended rank likelihood
• cognitive outcomes

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.

• 出版日期2013-12