Jackknife-blockwise empirical likelihood methods under dependence

Rongmao Zhang, Liang Peng, Yongcheng Qi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Empirical likelihood for general estimating equations is a method for testing hypothesis or constructing confidence regions on parameters of interest. If the number of parameters of interest is smaller than that of estimating equations, a profile empirical likelihood has to be employed. In case of dependent data, a profile blockwise empirical likelihood method can be used. However, if too many nuisance parameters are involved, a computational difficulty in optimizing the profile empirical likelihood arises. Recently, Li et al. (2011) [9] proposed a jackknife empirical likelihood method to reduce the computation in the profile empirical likelihood methods for independent data. In this paper, we propose a jackknife-blockwise empirical likelihood method to overcome the computational burden in the profile blockwise empirical likelihood method for weakly dependent data.

Original languageEnglish (US)
Pages (from-to)56-72
Number of pages17
JournalJournal of Multivariate Analysis
Volume104
Issue number1
DOIs
StatePublished - Feb 2012

Bibliographical note

Funding Information:
We thank two reviewers and an associate editor for their helpful comments. Zhang’s research was supported by NSFC grant 10801118 , Peng’s research was supported by NSA grant H98230-10-1-0170 and NSF grant DMS-1005336 , and Qi’s research was supported by NSA grant H98230-10-1-0161 and NSF grant DMS-1005345 .

Keywords

  • Confidence region
  • Empirical likelihood
  • General estimating equations
  • Jackknife
  • Weak dependence

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