| ����Ӧ�ķ���Hotelling's T^2���� |
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| 引用本文： | ��ʤ��,������,����կ. ����Ӧ�ķ���Hotelling's T^2����[J]. 应用概率统计, 2006, 35(3): 317-330. DOI: 10.3969/j.issn.1001-4268.2019.03.008 |
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| 作者姓名： | ��ʤ�� ������ ����կ |
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| 作者单位： | ?й????????????????, ????, 100049; ???????????????????????, ???, 330022 |
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| 摘 要： |
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| 关 键 词： | ??????任 ????? ?? ??Ч |
Group Hotelling's T^2 Test for Comparing Multiple Endpoints |
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| ZHANG Shenghu,ZHANG Sangu,LI Qizhai. Group Hotelling's T^2 Test for Comparing Multiple Endpoints[J]. Chinese Journal of Applied Probability and Statisties, 2006, 35(3): 317-330. DOI: 10.3969/j.issn.1001-4268.2019.03.008 |
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| Authors: | ZHANG Shenghu ZHANG Sangu LI Qizhai |
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| Affiliation: | School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China; School of Mathematics and Information Science,;Jiangxi Normal University, Nanchang, 330022, China |
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| Abstract: | ??Comparisons between two samples with multiple endpoints are often encountered in many real applications and Hotelling's T^2 test (HT) may suffer from loss of efficiency when multivariate normality assumption is violated. To overcome this issue, we propose a group Hotelling's T^2 test (GHT) where HT is conducted within each group after inverse normal transformation and then use the maximum value among combined statistics based on $p$-values at the group-level. Extensive simulations show that GHT is more robust than HT and some other existing procedures. Finally, the applications to plasma-renin activity in serum study and the ageing human brain further demonstrate the performance of GHT. |
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| Keywords: | inverse normal transformations multiple endpoints rank power |
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