TY - JOUR
T1 - Robust conditional Weibull-type estimation
AU - Goegebeur, Yuri
AU - Guillou, Armelle
AU - Rietsch, Theo
PY - 2015
Y1 - 2015
N2 - We study nonparametric robust tail coefficient estimation when the variable of interest, assumed to be of Weibull type, is observed simultaneously with a random covariate. In particular, we introduce a robust estimator for the tail coefficient, using the idea of the density power divergence, based on the relative excesses above a high threshold. The main asymptotic properties of our estimator are established under very general assumptions. The finite sample performance of the proposed procedure is evaluated by a small simulation experiment.
AB - We study nonparametric robust tail coefficient estimation when the variable of interest, assumed to be of Weibull type, is observed simultaneously with a random covariate. In particular, we introduce a robust estimator for the tail coefficient, using the idea of the density power divergence, based on the relative excesses above a high threshold. The main asymptotic properties of our estimator are established under very general assumptions. The finite sample performance of the proposed procedure is evaluated by a small simulation experiment.
KW - Density power divergence
KW - Local estimation
KW - Tail coefficient
KW - Weibull-type distribution
U2 - 10.1007/s10463-014-0458-9
DO - 10.1007/s10463-014-0458-9
M3 - Journal article
SN - 0020-3157
VL - 67
SP - 479
EP - 514
JO - Annals of the Institute of Statistical Mathematics
JF - Annals of the Institute of Statistical Mathematics
IS - 3
ER -