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Biometrika 1995 82(3):543-552; doi:10.1093/biomet/82.3.543
© 1995 by Biometrika Trust
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A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

C. GENEST, K. GHOUDI and L.-P. RIVEST

Département de mathématiques et de statistique, Université Laval, Québec, Canada G1K 7P4

Received for publication 1 June 1993. Revision received 1 January 1995.
  Abstract

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.

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


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