Multivariate partially linear single-index models: Bayesian analysis
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AbstractPartially 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.
All Author(s) ListPoon W.-Y., Wang H.-B.
Journal nameJournal of Nonparametric Statistics
Year2014
Month1
Day1
Volume Number26
Issue Number4
PublisherTaylor & Francis
Place of PublicationUnited Kingdom
Pages755 - 768
ISSN1048-5252
LanguagesEnglish-United Kingdom
Keywordsfree-knot spline, Gibbs sampler, reversible jump, single-index model

Last updated on 2021-16-01 at 00:30