A Test for Mean Vector and Simultaneous Confidence Intervals with Three-Step Monotone Missing Data
A Test for Mean Vector and Simultaneous Confidence Intervals with Three-Step Monotone Missing Data
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DOI：
10.1080/01966324.2014.911670
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发表时间：
2014-07
影响因子：
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通讯作者：
Ayaka Yagi;T. Seo
中科院分区：
文献类型：
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作者：
Ayaka Yagi;T. Seo
在这篇文章中，我们考虑的平均向量和同时置信区间的测试时，数据有一个三步单调模式，是缺失的观察。基于Jinadasa和Tracy（1992）的推导，给出了三步单调缺失数据模式下均值向量和协方差阵的极大似然估计.我们提出了一个近似的霍特林的T2型统计量的上百分位数来测试的平均向量。此外，我们得到了近似的同时置信区间的任何和所有的线性复合的平均值和平均分量的测试平等。最后，通过蒙特卡罗模拟研究了近似的精度，并给出了一个数值例子来说明该方法。
SYNOPTIC ABSTRACT In this article, we consider the problem of testing for mean vector and simultaneous confidence intervals when the data have a three-step monotone pattern that is missing observations. The maximum likelihood estimators of the mean vector and the covariance matrix with a three-step monotone missing data pattern are presented based on the derivation of Jinadasa and Tracy (1992). We propose an approximate upper percentile of Hotelling’s T2-type statistic to test the mean vector. Further, we obtain the approximate simultaneous confidence intervals for any and all linear compounds of the mean and the testing equality of mean components. Finally, the accuracy of the approximation is investigated by Monte Carlo simulation, and a numerical example is given to illustrate the method.