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Empirical likelihood based inference for generalized additive partial linear models

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  • Yu, Zhuoxi
  • Yang, Kai
  • Parmar, Milan

Abstract

Empirical-likelihood based inference for the parameters in generalized additive partial linear models (GAPLM) is investigated. With the use of the polynomial spline smoothing for estimation of nonparametric functions, an estimated empirical likelihood ratio statistic based on the quasi-likelihood equation is proposed. We show that the resulting statistic is asymptotically standard chi-squared distributed and the confidence regions for the parametric components are constructed. Some simulations are conducted to illustrate the proposed methods.

Suggested Citation

  • Yu, Zhuoxi & Yang, Kai & Parmar, Milan, 2018. "Empirical likelihood based inference for generalized additive partial linear models," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 105-112.
    • Handle: RePEc:eee:apmaco:v:339:y:2018:i:c:p:105-112
    DOI: 10.1016/j.amc.2018.06.050
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    References listed on IDEAS

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    1. Härdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2004. "Bootstrap Inference In Semiparametric Generalized Additive Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 265-300, April.
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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