Smoothing an indefinite variance-covariance matrix
Smoothing an indefinite variance-covariance matrix
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DOI：
10.1080/00949657908810316
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发表时间：
1979-08
影响因子：
1.2
通讯作者：
N. Schwertman;D.M Allen
中科院分区：
文献类型：
--
作者：
N. Schwertman;D.M Allen
通常的估计的分散矩阵有一个明显的优势，其他估计程序，因为它是计算上可行的数据集与大量的缺失观测。然而，当数据向量有一些缺失元素时，这个估计量可能不具有至少半正定的所需属性。本文提出的平滑方法弥补了这一不足。此外，计算程序的建议。通过一个例子说明了平滑过程，蒙特卡罗实验表明，平滑大大增加了Kleinbaum（1973）提出的检验的功效。
The usual estimator of the dispersion matrix has a distinct advantage over other estimation procedures since it is computationally feasible for a data set with a substantial number of missing observations. However, this estimator, when the data vectors have some missing elements, may not have the required property of being at least positive semidefinite. The smoothing procedure suggested in this paper rectifies this deficiency. In addition, com-putational procedures are proposed. The smoothing procedure is illustrated by an example and a Monte Carlo experiment shows that smoothing substantially increases the power of the test proposed by Kleinbaum(1973).