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Depth-based weighted empirical likelihood and general estimating equations

Author

Listed:
  • Yunlu Jiang
  • Shaoli Wang
  • Wenxiu Ge
  • Xueqin Wang

Abstract

In this paper, we link the depth-based weighted empirical likelihood (WEL) with general estimating equations to produce a robust estimation of parameters for contaminated data with auxiliary information about the parameters. Such auxiliary information can be expressed through a group of functionally independent general estimating equations. Under general conditions, asymptotic properties of the WEL estimator are established. Furthermore, we prove that the WEL ratio statistic is asymptotically chi-squared distributed. Simulation studies are conducted to test the robustness of the WEL estimator. Finally, we apply the proposed method to analyse the gilgai survey data.

Suggested Citation

  • Yunlu Jiang & Shaoli Wang & Wenxiu Ge & Xueqin Wang, 2011. "Depth-based weighted empirical likelihood and general estimating equations," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 1051-1062.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:4:p:1051-1062
    DOI: 10.1080/10485252.2011.594510
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    Cited by:

    1. Yongli Sang & Xin Dang & Yichuan Zhao, 2020. "Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 573-598, June.
    2. Xiaohui Liu & Qihua Wang & Yi Liu, 2017. "A consistent jackknife empirical likelihood test for distribution functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 249-269, April.
    3. Yunlu Jiang & Canhong Wen & Xueqin Wang, 2018. "Adaptive Exponential Power Depth with Application to Classification," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 466-480, October.

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