A Bayesian multivariate partially linear single-index probit model for ordinal responses |
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Authors: | Hai-Bin Wang |
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Institution: | School of Mathematical Sciences, Xiamen University, Xiamen, People's Republic of China |
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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. |
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Keywords: | Free-knot splines Gibbs sampler Ordinal data Probit models Single-index models |
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