A SEMIPARAMETRIC ESTIMATION PROCEDURE OF DEPENDENCE PARAMETERS IN MULTIVARIATE FAMILIES OF DISTRIBUTIONS
A SEMIPARAMETRIC ESTIMATION PROCEDURE OF DEPENDENCE PARAMETERS IN MULTIVARIATE FAMILIES OF DISTRIBUTIONS
复制标题
DOI：
10.1093/biomet/82.3.543
复制
发表时间：
1995-09-01
期刊：
影响因子：
2.7
通讯作者：
RIVEST, LP
中科院分区：
文献类型：
--
作者：
GENEST, C;GHOUDI, K;RIVEST, LP
本文研究了用于估计多元分布族中相关参数的半参数方法的特性。所提出的估计量是作为伪似然方程的解而获得的，被证明是一致的、渐近正态的并且在独立时完全有效。其渐近方差的自然估计量被证明是一致的。在双变量数据关联的克莱顿模型的特殊情况下，与替代半参数估计量进行了比较。
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.