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Multivariate partially linear single-index models: Bayesian analysis

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  • Wai-Yin Poon
  • Hai-Bin Wang

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.

Suggested Citation

  • Wai-Yin Poon & Hai-Bin Wang, 2014. "Multivariate partially linear single-index models: Bayesian analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 755-768, December.
    • Handle: RePEc:taf:gnstxx:v:26:y:2014:i:4:p:755-768
    DOI: 10.1080/10485252.2014.965706
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    References listed on IDEAS

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