Adjusted Jackknife Empirical Likelihood

2016/03/13 by Ying‐Ju Chen, Wei Ning, Chen, Ying-Ju +1
Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1603.04093

openalex publication_date 2016/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Jackknife empirical likelihood (JEL) is an effective modified version of empirical likelihood method (EL). Through the construction of the jackknife pseudo-values, JEL overcomes the computational difficulty of EL method when its constraints are nonlinear while maintaining the same asymptotic results for one sample and two-sample U statistics. In this paper, we propose an adjusted version of JEL to guarantee that the adjusted jackknife empirical likelihood (AJEL) statistic is well-defined for all the values of the parameter, instead of restricting on the convex hull of the estimation equation. The properties of JEL have been preserved for AJEL.

Citations

• Adjusted Empirical Likelihood and its Properties

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