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Model Averaging Estimation for Varying-Coefficient Single-Index Models

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Abstract

The varying-coefficient single-index model (VCSIM) is widely used in economics, statistics and biology. A model averaging method for VCSIM based on a Mallows-type criterion is proposed to improve prodictive capacity, which allows the number of candidate models to diverge with sample size. Under model misspecification, the asymptotic optimality is derived in the sense of achieving the lowest possible squared errors. The authors compare the proposed model averaging method with several other classical model selection methods by simulations and the corresponding results show that the model averaging estimation has a outstanding performance. The authors also apply the method to a real dataset.

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Author information

Authors and Affiliations

  1. School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Yue Liu

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Jiahui Zou

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Jiahui Zou

  4. School of Science, Minzu University of China, Beijing, 100081, China

    Shangwei Zhao

  5. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China

    Qinglong Yang

Authors
  1. Yue Liu
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  2. Jiahui Zou
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  3. Shangwei Zhao
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  4. Qinglong Yang
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Corresponding author

Correspondence to Jiahui Zou.

Additional information

This research was supported by the National Nature Science Foundation of China, under Grant Nos. 12001559 and 11971324 and the Ministry of Education of Humanities and Social Science project, under Grant No. 19YJC910008.

This paper was recommended for publication by Editor ZHANG Xinyu.

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Liu, Y., Zou, J., Zhao, S. et al. Model Averaging Estimation for Varying-Coefficient Single-Index Models. J Syst Sci Complex 35, 264–282 (2022). https://doi.org/10.1007/s11424-021-0158-5

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  • DOI: https://doi.org/10.1007/s11424-021-0158-5

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