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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.

Suggested Citation

  • 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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    Cited by:

    1. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    2. Zhao, Yan-Yong & Lin, Jin-Guan & Xu, Pei-Rong & Ye, Xu-Guo, 2015. "Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 204-221.

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