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A partial spline approach for semiparametric estimation of varying-coefficient partially linear models

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  • Kim, Young-Ju

Abstract

A semiparametric method based on smoothing spline is proposed for the estimation of varying-coefficient partially linear models. A simple and efficient method is proposed, based on a partial spline technique with a lower-dimensional approximation to simultaneously estimate the varying-coefficient function and regression parameters. For interval inference, Bayesian confidence intervals were obtained based on the Bayes models for varying-coefficient functions. The performance of the proposed method is examined both through simulations and by applying it to Boston housing data.

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  • Kim, Young-Ju, 2013. "A partial spline approach for semiparametric estimation of varying-coefficient partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 181-187.
    • Handle: RePEc:eee:csdana:v:62:y:2013:i:c:p:181-187
    DOI: 10.1016/j.csda.2013.01.006
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    References listed on IDEAS

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    6. Young-Ju Kim, 2010. "Semiparametric analysis for case-control studies: a partial smoothing spline approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 1015-1025.
    7. Young‐Ju Kim & Chong Gu, 2004. "Smoothing spline Gaussian regression: more scalable computation via efficient approximation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 337-356, May.
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    Cited by:

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