Extremal linear quantile regression with weibull-type tails

Fengyang He, Huixia Judy Wang, Tiejun Tong

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)


This study examines the estimation of extreme conditional quantiles for distributions with Weibull-type tails. We propose two families of estimators for the Weibull tail-coefficient, and construct an extrapolation estimator for the extreme conditional quantiles based on a quantile regression and extreme value theory. The asymptotic results of the proposed estimators are established. This work fills a gap in the literature on extreme quantile regressions, where many important Weibull-type distributions are excluded by the assumed strong conditions. A simulation study shows that the proposed extrapolation method provides estimations of the conditional quantiles of extreme orders that are more efficient and stable than those of the conventional method. The practical value of the proposed method is demonstrated through an analysis of extremely high birth weights.

Original languageEnglish
Pages (from-to)1357-1377
Number of pages21
JournalStatistica Sinica
Issue number3
Publication statusPublished - Jul 2020

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Asymptotic normality
  • Extrapolation method
  • Extreme conditional quantiles
  • Linear quantile regression
  • Weibull-type distributions


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