Testing equality of two normal covariance matrices with monotone missing data
Testing equality of two normal covariance matrices with monotone missing data
复制标题
使用单调缺失数据测试两个正态协方差矩阵的相等性
DOI：
10.1080/03610926.2019.1591453
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yafei He
中科院分区：
文献类型：
--
作者：
Jianqi Yu;K. Krishnamoorthy;Yafei He
摘要研究了两个多元正态协方差矩阵的等价性检验问题。假设不完全数据是单调模式，提出了一个类似于似然比检验统计量的量。得到了一个令人满意的量分布近似值。概述了基于近似分布的假设检验。利用蒙特卡罗模拟研究了该试验的优点。蒙特卡罗研究表明，该测试即使对中等小样本也是非常令人满意的。通过实例说明了所提出的方法。
Abstract The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.