## Article

#### Title: Remark on Extreme Regression Quantile

##### Issue: Volume 69 Part 1 Year 2007
###### Abstract
We consider the extreme regression quantile in the linear regression model and derive its asymptotic distribution under a density ($f$) of the errors with exponential tail, and under mild conditions on the regressors. The method is based on the fact that the slope components of the extreme regression quantile can be interpreted as a special $R$-estimator. Hence, we can use the methods based on ranks, in view of the uniform asymptotic linearity of the H\'ajek (1965) rank scores process. This $R$-estimator can even estimate the slope parameters consistently under some conditions on the tails of $f.$ The intercept component is the maximum of the pertaining residuals; its asymptotic distribution differs from that of the sample extreme in the i.i.d. case only in that it involves the hat matrix of the regressors. The rate of convergence is $f(F^{-1}(1-1/n))$ both for the slopes and the intercept, which is in conformity with the maximal domain of attraction of the errors.

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