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A Bayesian multivariate partially linear single-index probit model for ordinal responses
Authors:Hai-Bin Wang
Institution:School of Mathematical Sciences, Xiamen University, Xiamen, People's Republic of China
Abstract:Combining the multivariate probit models with the multivariate partially linear single-index models, we propose new semiparametric latent variable models for multivariate ordinal response data. Based on the reversible jump Markov chain Monte Carlo technique, we develop a fully Bayesian method with free-knot splines to analyse the proposed models. To address the problem that the ordinary Gibbs sampler usually converges slowly, we make use of the partial-collapse and parameter-expansion techniques in our algorithm. The proposed methodology are demonstrated by simulated and real data examples.
Keywords:Free-knot splines  Gibbs sampler  Ordinal data  Probit models  Single-index models
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