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Asymptotic Optimality of the Nonnegative Garrote Estimator Under Heteroscedastic Errors

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Abstract

This paper proposes the Nonnegative Garrote (NG) estimator for linear model with heteroscedastic errors. On the other hand, under some regularity conditions, the authors show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model average estimator. Simulation results and a real data analysis are reported for testing the results obtained previously. These results provide a stronger theoretical basis for the use of NG estimator by strengthening existing findings.

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Author information

Authors and Affiliations

  1. College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China

    Xiuping Chen, Guanghui Cai & Shangwei Zhao

  2. Department of Statistics, College of Science, Minzu University of China, Beijing, 100081, China

    Yan Gao

Authors
  1. Xiuping Chen
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  2. Guanghui Cai
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  3. Yan Gao
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  4. Shangwei Zhao
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Corresponding author

Correspondence to Guanghui Cai.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 61501331, the Natural Science Foundation of Zhejiang Province under Grant No. LY14F010002.

This paper was recommended for publication by Editor LI Qizhai.

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Chen, X., Cai, G., Gao, Y. et al. Asymptotic Optimality of the Nonnegative Garrote Estimator Under Heteroscedastic Errors. J Syst Sci Complex 33, 545–562 (2020). https://doi.org/10.1007/s11424-020-8270-5

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  • DOI: https://doi.org/10.1007/s11424-020-8270-5

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