Empirical likelihood for single-index models

Liu Gen Xue*, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

82 Citations (Scopus)


The empirical likelihood method is especially useful for constructing confidence intervals or regions of the parameter of interest. This method has been extensively applied to linear regression and generalized linear regression models. In this paper, the empirical likelihood method for single-index regression models is studied. An estimated empirical log-likelihood approach to construct the confidence region of the regression parameter is developed. An adjusted empirical log-likelihood ratio is proved to be asymptotically standard chi-square. A simulation study indicates that compared with a normal approximation-based approach, the proposed method described herein works better in terms of coverage probabilities and areas (lengths) of confidence regions (intervals).

Original languageEnglish
Pages (from-to)1295-1312
Number of pages18
JournalJournal of Multivariate Analysis
Issue number6
Publication statusPublished - Jul 2006

Scopus Subject Areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Confidence region
  • Empirical likelihood
  • Single-index model


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