Multivariate normal distribution approaches for dependently left-truncated datta
Multivariate normal distribution approaches for dependently left-truncated datta
复制标题
相关左截断数据的多元正态分布方法
DOI：
10.1007/s00362-010-0321-x
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Yoshihiko Konno
中科院分区：
文献类型：
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作者：
Takeshi Emura;Yoshihiko Konno
截断数据的许多统计方法依赖于关于截断变量的独立性假设。然而，在许多应用研究中，感兴趣的变量X与其截断变量L之间的依赖关系在数据结构建模中起着基础性的作用。对于截断数据，人们通常感兴趣的是估计（L，X）的边缘分布，并经常检查XandL之间的依赖程度。为了放松独立性假设，我们提出了一种在（L，X）上拟合参数模型的方法，该方法可以轻松地将依赖结构纳入截断机制。针对二元正态分布的一个具体例子，给出了分数方程和Fisher信息矩阵。一个强大的程序的基础上的双变量分布也被认为是。仿真结果验证了该方法在有限样本情况下的性能。扩展所提出的方法，双截断数据进行了简要讨论。
Many statistical methods for truncated data rely on the independence assumption regarding the truncation variable. In many application studies, however, the dependence between a variableXof interest and its truncation variableLplays a fundamental role in modeling data structure. For truncated data, typical interest is in estimating the marginal distributions of (L,X) and often in examining the degree of the dependence betweenXandL. To relax the independence assumption, we present a method of fitting a parametric model on (L,X), which can easily incorporate the dependence structure on the truncation mechanisms. Focusing on a specific example for the bivariate normal distribution, the score equations and Fisher information matrix are provided. A robust procedure based on the bivariatet-distribution is also considered. Simulations are performed to examine finite-sample performances of the proposed method. Extension of the proposed method to doubly truncated data is briefly discussed.