Empirical likelihood for single index model with missing covariates at random

Xu Guo, Cuizhen Niu, Yiping Yang, Wangli Xu

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


In this paper, we investigate the empirical-likelihood-based inference for the construction of confidence intervals and regions of the parameters of interest in single index models with missing covariates at random. An augmented inverse probability weighted-type empirical likelihood ratio for the parameters of interest is defined such that this ratio is asymptotically standard chi-squared. Our approach is to directly calibrate the empirical log-likelihood ratio, and does not need multiplication by an adjustment factor for the original ratio. Our bias-corrected empirical likelihood is self-scale invariant and no plug-in estimator for the limiting variance is needed. Some simulation studies are carried out to assess the performance of our proposed method.

Original languageEnglish (US)
Pages (from-to)588-601
Number of pages14
Issue number3
StatePublished - May 4 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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