A Bayesian Semiparametric Latent Variable Model for Mixed Responses View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-09

AUTHORS

Ludwig Fahrmeir, Alexander Raach

ABSTRACT

In this paper we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric Gaussian regression model. We extend existing LVMs with the usual linear covariate effects by including nonparametric components for nonlinear effects of continuous covariates and interactions with other covariates as well as spatial effects. Full Bayesian modelling is based on penalized spline and Markov random field priors and is performed by computationally efficient Markov chain Monte Carlo (MCMC) methods. We apply our approach to a German social science survey which motivated our methodological development. More... »

PAGES

327

Journal

TITLE

Psychometrika

ISSUE

3

VOLUME

72

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11336-007-9010-7

DOI

http://dx.doi.org/10.1007/s11336-007-9010-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046992230


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