Estimating the conditional tail index by integrating a kernel conditional quantile estimator

A Smooth Nonparametric Conditional Quantile Frontier Estimator

Traditional estimators for nonparametric frontier models (DEA, FDH) are very sensitive to extreme values/outliers. Recently, Aragon, Daouia, and Thomas-Agnan (2005) proposed a nonparametric α-frontier model and estimator based on a suitably defined conditional quantile which is more robust to extreme values/outliers. Their estimator is based on a nonsmooth empirical conditional distribution. In...

Estimation of the conditional tail index using a smoothed local Hill estimator

Kernel and Nearest Neighbor Estimation of a Conditional Quantile

Let (Xl'Z1)' (X2,Z2),· .. ,(Xn,Zn) be LLd. as (X,Z), Z taking values in Rl, and for o < p < 1, let ep(x) denote the conditional p-quantile of Z given X=x, i.e., P(Z ~ ep(x) IX=x) = p. In this paper, kernel and nearest neighbor estimators of ep(x) are proposed. As a first step in studying the asymptotics of these estimates, Bahadur type ~ representations of the sample conditional quantile functi...

A moment estimator for the conditional extreme-value index

In extreme value theory, the so-called extreme-value index is a parameter that controls the behavior of a distribution function in its right tail. Knowing this parameter is thus essential to solve many problems related to extreme events. In this paper, the estimation of the extreme-value index is considered in the presence of a random covariate, whether the conditional distribution of the varia...

CQN (Conditional Quantile Normalization)