A SEMIPARAMETRIC ESTIMATION PROCEDURE OF DEPENDENCE PARAMETERS IN MULTIVARIATE FAMILIES OF DISTRIBUTIONS

被引：362

作者：

GENEST, C

GHOUDI, K

RIVEST, LP

机构：

[1] Département de mathématiques et de statistique, Université, Laval, Québec

来源：

BIOMETRIKA | 1995年 / 82卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

ASYMPTOTIC THEORY; CLAYTONS BIVARIATE FAMILY; KENDALLS TAN; MULTIVARIATE RANK STATISTIC; PSEUDO-LIKELIHOOD; SEMIPARAMETRIC ESTIMATION;

D O I：

10.1093/biomet/82.3.543

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper investigates the properties of a semiparametric method for estimating the dependence parameters in a family of multivariate distributions. The proposed estimator, obtained as a solution of a pseudo-likelihood equation, is shown to be consistent, asymptotically normal and fully efficient at independence. A natural estimator of its asymptotic variance is proved to be consistent. Comparisons are made with alternative semiparametric estimators in the special case of Clayton's model for association in bivariate data.

引用

页码：543 / 552

页数：10

共 20 条

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[Anonymous], 1959, PUBL I STAT U PARIS, V8, P229

[2]

[Anonymous], 1994, J NONPARAMETR STAT, DOI DOI 10.1080/10485259408832593

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