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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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    1. Li, Qi & Racine, Jeffrey S., 2010. "Smooth Varying-Coefficient Estimation And Inference For Qualitative And Quantitative Data," Econometric Theory, Cambridge University Press, vol. 26(6), pages 1607-1637, December.
    2. Jianqing Fan & Qiwei Yao & Zongwu Cai, 2003. "Adaptive varying‐coefficient linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 57-80, February.
    3. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    4. Yingcun Xia, 2004. "Efficient estimation for semivarying-coefficient models," Biometrika, Biometrika Trust, vol. 91(3), pages 661-681, September.
    5. Chiang C-T. & Rice J. A & Wu C. O, 2001. "Smoothing Spline Estimation for Varying Coefficient Models With Repeatedly Measured Dependent Variables," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 605-619, June.
    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.
    8. R. L. Eubank & Chunfeng Huang & Y. Muñoz Maldonado & Naisyin Wang & Suojin Wang & R. J. Buchanan, 2004. "Smoothing spline estimation in varying‐coefficient models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 653-667, August.
    9. Zhang, Wenyang & Lee, Sik-Yum & Song, Xinyuan, 2002. "Local Polynomial Fitting in Semivarying Coefficient Model," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 166-188, July.
    10. Robert T. Krafty & Phyllis A. Gimotty & David Holtz & George Coukos & Wensheng Guo, 2008. "Varying Coefficient Model with Unknown Within-Subject Covariance for Analysis of Tumor Growth Curves," Biometrics, The International Biometric Society, vol. 64(4), pages 1023-1031, December.
    11. Damla Şentürk & Hans-Georg Müller, 2008. "Generalized varying coefficient models for longitudinal data," Biometrika, Biometrika Trust, vol. 95(3), pages 653-666.
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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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