
handle: 10754/678989
Summary: 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.
extrapolation method, Reliability and life testing, Statistics of extreme values; tail inference, asymptotic normality, extreme conditional quantiles, Nonparametric regression and quantile regression, Weibull-type distributions, linear quantile regression, Applications of statistics to biology and medical sciences; meta analysis
extrapolation method, Reliability and life testing, Statistics of extreme values; tail inference, asymptotic normality, extreme conditional quantiles, Nonparametric regression and quantile regression, Weibull-type distributions, linear quantile regression, Applications of statistics to biology and medical sciences; meta analysis
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