Multivariate partially linear single-index models: Bayesian analysis |
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Authors: | Wai-Yin Poon |
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Institution: | Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong, People's Republic of China |
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Abstract: | Partially linear single-index models play important roles in advanced non-/semi-parametric statistics due to their generality and flexibility. We generalise these models from univariate response to multivariate responses. A Bayesian method with free-knot spline is used to analyse the proposed models, including the estimation and the prediction, and a Metropolis-within-Gibbs sampler is provided for posterior exploration. We also utilise the partially collapsed idea in our algorithm to speed up the convergence. The proposed models and methods of analysis are demonstrated by simulation studies and are applied to a real data set. |
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Keywords: | free-knot spline Gibbs sampler reversible jump single-index model |
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