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

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

Research output: Contribution to journalArticlepeer-review

744 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 languageEnglish
Pages (from-to)543-552
Number of pages10
JournalBiometrika
Volume82
Issue number3
DOIs
Publication statusPublished - Sep 1995
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

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

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