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dc.contributor.authorFahrmeir, Ludwigen_US
dc.contributor.authorRaach, Alexanderen_US
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_US
dc.publisher|aTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen|cMünchenen_US
dc.relation.ispartofseries|aDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München|x471en_US
dc.subject.keywordLatent variable modelsen_US
dc.subject.keywordmixed responsesen_US
dc.subject.keywordpenalized splinesen_US
dc.subject.keywordspatial effectsen_US
dc.titleA Bayesian semiparametric latent variable model for mixed responsesen_US
dc.type|aWorking Paperen_US

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