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Model averaging by jackknife criterion for varying-coefficient partially linear models

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  • Guozhi Hu
  • Weihu Cheng
  • Jie Zeng

Abstract

This paper is concerned with model averaging procedure for varying-coefficient partially linear models. We proposed a jackknife model averaging method that involves minimizing a leave-one-out cross-validation criterion, and developed a computational shortcut to optimize the cross-validation criterion for weight choice. The resulting model average estimator is shown to be asymptotically optimal in terms of achieving the smallest possible squared error. The simulation studies have provided evidence of the superiority of the proposed procedures. Our approach is further applied to a real data.

Suggested Citation

  • Guozhi Hu & Weihu Cheng & Jie Zeng, 2020. "Model averaging by jackknife criterion for varying-coefficient partially linear models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(11), pages 2671-2689, June.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:11:p:2671-2689
    DOI: 10.1080/03610926.2019.1580736
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    Cited by:

    1. Guozhi Hu & Weihu Cheng & Jie Zeng, 2023. "Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data," Mathematics, MDPI, vol. 11(3), pages 1-21, February.
    2. Jie Zeng & Weihu Cheng & Guozhi Hu, 2023. "Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses," Mathematics, MDPI, vol. 11(8), pages 1-21, April.

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