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Jackknife Model Averaging for Quantile Single-Index Coefficient Model

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Abstract

In the past two decades, model averaging, as a way to solve model uncertainty, has attracted more and more attention. In this paper, the authors propose a jackknife model averaging (JMA) method for the quantile single-index coefficient model, which is widely used in statistics. Under model misspecification, the model averaging estimator is proved to be asymptotically optimal in terms of minimizing out-of-sample quantile loss. Simulation experiments are conducted to compare the JMA method with several model selections and model averaging methods, and the results show that the proposed method has a satisfactory performance. The method is also applied to a real dataset.

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Zhejiang University, Hangzhou, 310058, China

    Xianwen Sun & Lixin Zhang

  2. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China

    Lixin Zhang

Authors
  1. Xianwen Sun
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  2. Lixin Zhang
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Corresponding authors

Correspondence to Xianwen Sun or Lixin Zhang.

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The authors declare no conflict of interest.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. U23A2064 and 12031005.

This paper was recommended for publication by Editor LI Qizhai.

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Cite this article

Sun, X., Zhang, L. Jackknife Model Averaging for Quantile Single-Index Coefficient Model. J Syst Sci Complex 37, 1685–1713 (2024). https://doi.org/10.1007/s11424-024-3111-6

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  • DOI: https://doi.org/10.1007/s11424-024-3111-6

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