Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/31126 
Year of Publication: 
2006
Series/Report no.: 
Discussion Paper No. 471
Publisher: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Abstract: 
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.
Subjects: 
Latent variable models
mixed responses
penalized splines
spatial effects
MCMC
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
599.96 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.