Nonparametric estimation of the dependence function in bivariate extreme value distributions

被引：15

作者：

Jiménez, JR ^{[1
]}

Villa-Diharce, E

Flores, M

机构：

[1] Univ Texas, Austin, TX 78712 USA

[2] Ctr Invest Matemat AC, Guanajuato, Mexico

[3] Natl Pk Serv, Washington, DC 20240 USA

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2001年 / 76卷 / 02期

基金：

美国国家科学基金会;

关键词：

empirical distribution function; greatest convex minorant; weak convergence; Gaussian process;

D O I：

10.1006/jmva.2000.1931

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The paper considers the problem of estimating the dependence function of a bivariate extreme survival function with standard exponential marginals. Nonparametric estimators for the dependence function are proposed and their strong uniform convergence under suitable conditions is demonstrated. Comparisons of the proposed estimators with other estimators are made in terms of bias and mean squared error. Several real data sets from various applications are used to illustrate the procedures. (C) 2001 Academic Press.

引用

页码：159 / 191

页数：33

共 29 条

[1]

[Anonymous], 1964, Handbook of mathematical functions

[2] A nonparametric estimation procedure for bivariate extreme value copulas [J].