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- W2077762306 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." @default.
- W2077762306 created "2016-06-24" @default.
- W2077762306 creator A5056484501 @default.
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- W2077762306 date "2014-10-02" @default.
- W2077762306 modified "2023-09-26" @default.
- W2077762306 title "Multivariate partially linear single-index models: Bayesian analysis" @default.
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- W2077762306 doi "https://doi.org/10.1080/10485252.2014.965706" @default.
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