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

被引：304
|
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.
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页码：543 / 552
页数：10
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