The Annals of Statistics

Extremal quantile regression

Victor Chernozhukov

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Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. This paper develops a theory of quantile regression in the tails. Specifically, it obtains the large sample properties of extremal (extreme order and intermediate order) quantile regression estimators for the linear quantile regression model with the tails restricted to the domain of minimum attraction and closed under tail equivalence across regressor values. This modeling setup combines restrictions of extreme value theory with leading homoscedastic and heteroscedastic linear specifications of regression analysis. In large samples, extreme order regression quantiles converge weakly to arg min functionals of stochastic integrals of Poisson processes that depend on regressors, while intermediate regression quantiles and their functionals converge to normal vectors with variance matrices dependent on the tail parameters and the regressor design.

Article information

Ann. Statist., Volume 33, Number 2 (2005), 806-839.

First available in Project Euclid: 26 May 2005

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G32: Statistics of extreme values; tail inference 62G30: Order statistics; empirical distribution functions 62P20: Applications to economics [See also 91Bxx]
Secondary: 62E30 62J05: Linear regression

Conditional quantile estimation regression extreme value theory


Chernozhukov, Victor. Extremal quantile regression. Ann. Statist. 33 (2005), no. 2, 806--839. doi:10.1214/009053604000001165.

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