Comparisons between two samples with multiple endpoints are often encountered in many real applications and Hotelling's T2 test (HT) may suffer from loss of efficiency when multivariate normality assumption is violated.To overcome this issue,we propose a group Hotelling's T2 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.%两样本的多响应比较在实际中应用非常广泛.当样本不服从正态分布时,Hotelling's T2检验(HT)的功效普遍不高.为了解决这一问题,本文提出了分组Hotelling's T2检验(GHT),即对数据进行逆正态变换后,在每一组中进行HT,然后基于每组的p值构造统计量并取最大值.大量模拟表明,GHT比HT和其他已有检验更加稳健.最后,应用于血浆肾素活性和大脑衰老数据进一步验证GHT的有效性.
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