Empirical likelihood inference for monotone index model

Taisuke Otsu, Keisuke Takahata, Mengshan Xu

Research output: Contribution to journalArticlepeer-review


This paper proposes an empirical likelihood inference method for monotone index models. We construct the empirical likelihood function based on a modified score function developed by Balabdaoui et al. (Scand J Stat 46:517–544, 2019), where the monotone link function is estimated by isotonic regression. It is shown that the empirical likelihood ratio statistic converges to a weighted chi-squared distribution. We suggest inference procedures based on an adjusted empirical likelihood statistic that is asymptotically pivotal, and a bootstrap calibration with recentering. A simulation study illustrates usefulness of the proposed inference methods.

Original languageEnglish
JournalJapanese Journal of Statistics and Data Science
Publication statusAccepted/In press - 2023


  • Empirical likelihood
  • Isotonic regression
  • Monotone index model

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Theory and Mathematics


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