A Bayesian semiparametric latent variable model for mixed responses

Fahrmeir, Ludwig and Raach, Alexander (2006): A Bayesian semiparametric latent variable model for mixed responses. Collaborative Research Center 386, Discussion Paper 471

Abstract

In this article 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 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.

• Item Type: Paper (Research Paper)
• Keywords: Latent variable models, mixed responses, penalized splines, spatial effects, MCMC
• Subjects: Mathematics, Computer Science and Statistics; Mathematics, Computer Science and Statistics > Statistics; Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386
• Dewey Classification: 600 Natural sciences and mathematics; 600 Natural sciences and mathematics > 510 Mathematics