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Jackknife model averaging for additive expectile prediction

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  • Xianwen Sun
  • Lixin Zhang

Abstract

In the past 20 years, model averaging has been developed as a better tool than model selection in statistical prediction. Expectile prediction is widely used for modeling data with the heterogeneous conditional distribution. In this article, we introduce a model averaging estimator for additive expectile prediction. The resulting model averaging estimator is shown to have asymptotic optimality under some regular conditions. Simulation experiments are conducted to demonstrate that the performance of our method is better than that of other common model selection and model averaging methods under the finite-sample case. Our method is also verified in the house and wage datasets.

Suggested Citation

  • Xianwen Sun & Lixin Zhang, 2024. "Jackknife model averaging for additive expectile prediction," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(19), pages 6799-6831, October.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:19:p:6799-6831
    DOI: 10.1080/03610926.2023.2251625
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