ESTIMATING A MULTIDIMENSIONAL EXTREME-VALUE DISTRIBUTION

被引：27

作者：

EINMAHL, JHJ ^{[1
]}

DEHAAN, L ^{[1
]}

HUANG, X ^{[1
]}

机构：

[1] ERASMUS UNIV ROTTERDAM,3000 DR ROTTERDAM,NETHERLANDS

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 1993年 / 47卷 / 01期

关键词：

ASYMPTOTIC NORMALITY; CONSISTENCY; ESTIMATION; MULTIVARIATE EXTREMES;

D O I：

10.1006/jmva.1993.1069

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：

Let F and G be multivariate probability distribution functions, each with equal one dimensional marginals, such that there exists a sequence of constants an > 0, n ∈ N, with [formula] for all continuity points (x1, …, xd) of G. The distribution function G is characterized by the extreme-value index (determining the marginals) and the so-called angular measure (determining the dependence structure). In this paper, a non-parametric estimator of G, based on a random sample from F, is proposed. Consistency as well as asymptotic normality are proved under certain regularity conditions. © 1993 Academic Press, Inc.

引用

页码：35 / 47

页数：13

共 19 条

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[2]

Billingsley P, 1968, CONVERGENCE PROBABIL

[3]

DEHAAN L, 1977, Z WAHRSCHEINLICHKEIT, V40, P317

[4]

DEHAAN L, 1985, 45TH P SESS ISI AMST