A Bayesian semiparametric latent variable model for mixed responses - Ludwig Fahrmeir; Alexander Raach (Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, 2006)
In 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.
urn:nbn:de:bvb:19-epub-1839-8
Keywords: 519; Latent variable models; mixed responses; penalized splines; spatial effects; MCMC