Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations

• Yongli Sang ^[1] ; Xin Dang ^[2] ; Yichuan Zhao ^[3]
  1. [1] University of Louisiana at Lafayette

     University of Louisiana at Lafayette

     Estados Unidos

     
  2. [2] University of Mississippi

     University of Mississippi

     Estados Unidos

     
  3. [3] Georgia State University

     Georgia State University

     Estados Unidos

     

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 29, Nº. 2, 2020, págs. 573-598
• Idioma: inglés
• DOI: 10.1007/s11749-019-00667-1
• Texto completo no disponible (Saber más ...)
• Resumen

  • In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with U-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the computation complexity of the empirical likelihood (EL) method. However, as EL, JEL suffers the sensitivity problem to outliers. In this paper, we propose a weighted jackknife empirical likelihood (WJEL) to tackle the above limitation of JEL. The proposed WJEL tilts the JEL function by assigning smaller weights to outliers. The asymptotic of the WJEL ratio statistic is derived. It converges in distribution to a multiple of a chi-square random variable. The multiplying constant depends on the weighting scheme. The self-normalized version of WJEL ratio does not require to know the constant and hence yields the standard chi-square distribution in the limit. Robustness of the proposed method is illustrated by simulation studies and one real data application.

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