Power Prediction in Large Scale Multiple Testing: A Fourier Approach
A problem that is frequently found in large-scale multiple testing is that, in the present stage of experiment (e.g. gene microarray, functional MRI), the signals are so faint that it is impossible to attain a desired level of testing power, and one has to enroll more samples in the follow-up experi...
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| Format: | Others |
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Research Showcase @ CMU
2010
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| Online Access: | http://repository.cmu.edu/dissertations/2 http://repository.cmu.edu/cgi/viewcontent.cgi?article=1001&context=dissertations |
| Summary: | A problem that is frequently found in large-scale multiple testing is that, in the present stage of experiment (e.g. gene microarray, functional MRI), the signals are so faint that it is impossible to attain a desired level of testing power, and one has to enroll more samples in the follow-up experiment. Suppose we are going to enlarge the sample size by a times in the follow-up experiment, where a > 1 is not necessary an integer. A problem of great interest is, given data based on the current stage of experiment, how to predict the testing power after the sample size is enlarged by a times.
We consider test z-scores and model the test statistics in the current experiment as Xj ~ N(μj , 1), 1 ≤ j ≤ n. We propose a Fourier approach to predicting the testing power after n replicates. The approach produces a very accurate prediction for moderately large values of a ( a ≤ 7). Finally, we discuss potential applications of this method on real data with emphasis on gene microarray data. |
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