Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/31126 
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dc.contributor.authorFahrmeir, Ludwigen
dc.contributor.authorRaach, Alexanderen
dc.date.accessioned2006-04-25-
dc.date.accessioned2010-05-14T10:14:23Z-
dc.date.available2010-05-14T10:14:23Z-
dc.date.issued2006-
dc.identifier.pidoi:10.5282/ubm/epub.1839en
dc.identifier.piurn:nbn:de:bvb:19-epub-1839-8en
dc.identifier.urihttp://hdl.handle.net/10419/31126-
dc.description.abstractIn this article we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variable are modelles through a flexible semiparametric predictor. We extend existing LVM with simple 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 large German social science survey which motivated our methodological development.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x471en
dc.subject.ddc519en
dc.subject.keywordLatent variable modelsen
dc.subject.keywordmixed responsesen
dc.subject.keywordpenalized splinesen
dc.subject.keywordspatial effectsen
dc.subject.keywordMCMCen
dc.titleA Bayesian semiparametric latent variable model for mixed responses-
dc.type|aWorking Paperen
dc.identifier.ppn510830129en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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