A Bayesian multivariate partially linear single-index probit model for ordinal responses. Issue 8 (24th May 2018)

• Record Type: Journal Article
• Authors: Wang, Hai-Bin
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 8(2018)
• Page Start: 1616
• Page End: 1636
• Publication Date: 2018-05-24
• Subjects: Free-knot splines -- Gibbs sampler -- Ordinal data -- Probit models -- Single-index models; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1442469
• Languages: English
• ISSNs: 0094-9655

Abstract

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.