Statistical Tests Based on Transformed Data
Statistical Tests Based on Transformed Data
复制标题
基于转换数据的统计测试
DOI：
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发表时间：
1983
期刊：
影响因子：
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通讯作者：
Chi
中科院分区：
文献类型：
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作者：
K. Doksum;Chi
摘要研究了线性模型中原始数据经过变换后的假设检验问题。假设变换涉及必须从数据估计的未知参数。对于某些重要的检验问题，发现渐近水平和功效就好像λ已被假定为已知。渐近效率结果表明，当使用Box-Cox变换时，基于变换数据的检验具有良好的功效特性。转换后的两个样本和线性回归测试问题的模拟结果表明，这是真实的中等到小样本量以及。特别是，与通常的t检验和秩检验相比，基于修剪变换变量平均值的α修剪t检验在轻尾和重尾偏斜模型中表现得非常好。
Abstract The problem of testing hypotheses in linear models when the original data have been transformed is considered. It is assumed that the transformation involves an unknown parameter that has to be estimated from the data. For certain important testing problems it is found that the asymptotic level and power is as if λ had been assumed known. Asymptotic efficiency results show that when the Box-Cox transformation is used, tests based on transformed data have good power properties. Simulation results for transformed two-sample and linear regression testing problems show this to be true for moderate to small sample sizes as well. In particular, an α-trimmed t test based on averages of trimmed transformed variables performs very well in both light-tailed and heavy-tailed skew models when compared with the usual t test and rank tests.