A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

C. Genest; K. Ghoudi; L. p. Rivest

doi:10.1093/biomet/82.3.543

A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

C. Genest, K. Ghoudi, L. p. Rivest

Research output: Contribution to journal › Article › peer-review

835 Citations (Scopus)

Abstract

SUMMARY: 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.

Original language	English
Pages (from-to)	543-552
Number of pages	10
Journal	Biometrika
Volume	82
Issue number	3
DOIs	https://doi.org/10.1093/biomet/82.3.543
Publication status	Published - Sept 1995
Externally published	Yes

Keywords

Asymptotic theory
Clayton's bivariate family
Kendall's tau
Multivariate rank statistic
Pseudo-likelihood
Semiparametric estimation

ASJC Scopus subject areas

Statistics and Probability
General Mathematics
Agricultural and Biological Sciences (miscellaneous)
General Agricultural and Biological Sciences
Statistics, Probability and Uncertainty
Applied Mathematics

Access to Document

10.1093/biomet/82.3.543

Cite this

@article{48292618e307482d9b66c729771dfaf4,

title = "A semiparametric estimation procedure of dependence parameters in multivariate families of distributions",

abstract = "SUMMARY: 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.",

keywords = "Asymptotic theory, Clayton's bivariate family, Kendall's tau, Multivariate rank statistic, Pseudo-likelihood, Semiparametric estimation",

author = "C. Genest and K. Ghoudi and Rivest, {L. p.}",

note = "Funding Information: Research funds in partial support of this work were granted by the Natural Sciences and Engineering Research Council of Canada and by the Fonds pour la formation de chercheurs et l'aide a la recherche du Gouvernement du Quebec.",

year = "1995",

month = sep,

doi = "10.1093/biomet/82.3.543",

language = "English",

volume = "82",

pages = "543--552",

journal = "Biometrika",

issn = "0006-3444",

publisher = "Oxford University Press",

number = "3",

}

TY - JOUR

T1 - A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

AU - Genest, C.

AU - Ghoudi, K.

AU - Rivest, L. p.

N1 - Funding Information: Research funds in partial support of this work were granted by the Natural Sciences and Engineering Research Council of Canada and by the Fonds pour la formation de chercheurs et l'aide a la recherche du Gouvernement du Quebec.

PY - 1995/9

Y1 - 1995/9

N2 - SUMMARY: 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.

AB - SUMMARY: 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.

KW - Asymptotic theory

KW - Clayton's bivariate family

KW - Kendall's tau

KW - Multivariate rank statistic

KW - Pseudo-likelihood

KW - Semiparametric estimation

UR - http://www.scopus.com/inward/record.url?scp=33646533039&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646533039&partnerID=8YFLogxK

U2 - 10.1093/biomet/82.3.543

DO - 10.1093/biomet/82.3.543

M3 - Article

AN - SCOPUS:33646533039

SN - 0006-3444

VL - 82

SP - 543

EP - 552

JO - Biometrika

JF - Biometrika

IS - 3

ER -

A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this