A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distributions
研究了一种半参数方法估计多元分布中相依参数的性质，证明该估计量在独立情况下一致、渐近正态且完全有效，并给出了渐近方差的一致估计量。
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.