Empirical likelihood inference for semi-parametric estimating equations

Qin and Lawless (1994) established the statistical inference theory for the empirical likelihood of the general estimating equations. However, in many practical problems, some unknown functional parts h(t) appear in the corresponding estimating equations E F G(X, h(T), β) = 0. In this paper, the empirical likelihood inference of combining information about unknown parameters and distribution function through the semi-parametric estimating equations are developed, and the corresponding Wilk's Theorem is established. The simulations of several useful models are conducted to compare the finite-sample performance of the proposed method and that of the normal approximation based method. An illustrated real example is also presented.



Work Title Empirical likelihood inference for semi-parametric estimating equations
Open Access
  1. ShanShan Wang
  2. HengJian Cui
  3. RunZe Li
  1. Confidence region
  2. Coverage probability
  3. Empirical likelihood ratio
  4. Semi-parametric estimating equation
  5. Wilk’s theorem
License In Copyright (Rights Reserved)
Work Type Article
  1. Science China Mathematics
Publication Date September 14, 2012
Publisher Identifier (DOI)
  1. https://doi.org/10.1007/s11425-012-4494-8
Deposited July 19, 2022




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Work History

Version 1

  • Created
  • Added Wang2013_Article_EmpiricalLikelihoodInferenceFo.pdf
  • Added Creator Shan Shan Wang
  • Added Creator Heng Jian Cui
  • Added Creator Run Ze Li
  • Published
  • Updated Keyword, Publication Date Show Changes
    • Confidence region, Coverage probability, Empirical likelihood ratio, Semi-parametric estimating equation, Wilk’s theorem
    Publication Date
    • 2013-06-01
    • 2012-09-14
  • Renamed Creator ShanShan Wang Show Changes
    • Shan Shan Wang
    • ShanShan Wang
  • Renamed Creator HengJian Cui Show Changes
    • Heng Jian Cui
    • HengJian Cui
  • Renamed Creator RunZe Li Show Changes
    • Run Ze Li
    • RunZe Li
  • Updated