A semiparametric approach to mixed outcome latent variable models: Estimating the association between cognition and regional brain volumes

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.

• Publication year

  2013

• Venue

  arXiv: Applications

• Publication date

  2013-12-01

• Fields of study

  Mathematics, Computer Science, Psychology

• Identifiers
  DOI 10.1214/13-AOAS675arXiv 1401.2728
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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