semiparametric; Generalized Pareto; single index; extreme events; curse-of-dimensionality; dimension reduction
Abstract :
[en] In this paper, we consider a regression model in which the tail of the conditional
distribution of the response can be approximated by a Generalized Pareto distribution.
Our model is based on a semiparametric single-index assumption on the
conditional tail index; while no further assumption on the conditional scale
parameter is made. The underlying dimension reduction assumption allows the procedure
to be of prime interest in the case where the dimension of the covariates
is high, in which case the purely nonparametric techniques fail while the purely
parametric ones are too rough to correctly fit to the data. We derive asymptotic
normality of the estimators that we define, and propose an iterative algorithm in
order to perform their practical implementation. Our results are supported by some
simulations and a practical application on a public database of operational losses.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Hambuckers, julien ; Université de Liège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie
Heuchenne, Cédric ; Université de Liège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie
Lopez, Olivier
Language :
English
Title :
A semiparametric model for Generalized Pareto regression based on a dimension reduction assumption
