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Updated: Sep 14, 2026
Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
A SEMIPARAMETRIC APPROACH TO MIXED OUTCOME LATENT VARIABLE MODELS: ESTIMATING THE ASSOCIATION BETWEEN COGNITION AND
Jonathan Gruhl1, Elena A Erosheva1, Paul K Crane1
1University of Washington.
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 (2007), 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.
