Empirical likelihood in some nonparametric and semiparametric models

Liugen Xue, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this very selective overview, we summarise the recent developments by our own and other, on the empirical likelihood in some nonparametric and semiparametric regression models. The models include the partially linear model, the single-index model, the partially linear singleindex model, the varying coefficient model, and so on. The focus of this overview is to expatiate the adjustment and "bias correction" methodologies when Wilks' phenomenon does not hold. The adjustment or bias correction can make the limiting distributions tractable such that they can be directly used to construct the confidence regions of parameters of interest without the assistance of Monte Carlo approximation.

Original languageEnglish
Pages (from-to)367-378
Number of pages12
JournalStatistics and its Interface
Volume5
Issue number3
DOIs
Publication statusPublished - 2012

Scopus Subject Areas

  • Statistics and Probability
  • Applied Mathematics

User-Defined Keywords

  • Adjustment
  • Bias correction
  • Confidence region
  • Empirical likelihood
  • Semiparametric regression models
  • Wilks' phenomenon

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