【摘 要】
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Large-scale two-sample Students t testing problems often arise from the statistical analysis of scientific data.To detect the positions of different values
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Large-scale two-sample Students t testing problems often arise from the statistical analysis of scientific data.To detect the positions of different values between two mean vectors,a famous procedure is applying the Benjamini and Hochberg(B-H)method together with two-sample Students t statistics to control the false discovery rate(FDR).In many applications,the population mean vectors are often expected to be sparse or asymptotically sparse.For example,in genetics,a quantitative trait could be controlled by a few major genes and many polygenes,and it is typically assumed that the polygenes have vanishingly small effects.When dealing with this type of data,can we gain more power than the standard procedure such as the B-H method with Students t statistics while keeping the FDR under control? The answer is YES!
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