Regression with response distributions of Pareto-type

Authors:
Jan Beirlant;Yuri Goegebeur
Affiliations:
Department of Mathematics, Katholieke Universiteit Leuven, Celestijnenlaan 200B, B-3001 Leuven, Belgium;Department of Applied Economics, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium
Venue:
Computational Statistics & Data Analysis
Year:
2003

Citing 1
Cited 6

Parameter and quantile estimation for the generalized pareto distribution

Technometrics

Local polynomial maximum likelihood estimation for Pareto-type distributions

Journal of Multivariate Analysis
A goodness-of-fit statistic for Pareto-type behaviour

Journal of Computational and Applied Mathematics - Special issue: Jef Teugels
A moving window approach for nonparametric estimation of the conditional tail index

Journal of Multivariate Analysis
Functional nonparametric estimation of conditional extreme quantiles

Journal of Multivariate Analysis
A goodness-of-fit statistic for Pareto-type behaviour

Journal of Computational and Applied Mathematics - Special issue: Jef Teugels
A note on tail dependence regression

Journal of Multivariate Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

The estimation of the Pareto index in presence of covariate information is discussed. The Pareto index is modelled as a function of the explanatory variables and hence measures the tail heaviness of the conditional distribution of the response variable given this covariate information. The original response data are transformed in order to obtain generalized residuals, possessing a common Pareto-type distribution. An exponential regression model will be developed for these generalized residuals. The parameters of this model are estimated using a profile likelihood method. The resulting maximum likelihood estimates of the regression coefficients can be used for the estimation of extreme quantiles of the conditional distribution of the dependent variable. The methods developed are illustrated with two practical examples.