| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 97 === The FDR (False Discovery Rate) is the expectation of proportion of the rejected true null hypotheses among all rejected hypotheses. In large-scale multiple testing problems, Benjamini and Hochberg (1995) developed the multiple testing procedure (BH procedure) based on the p values of the individual tests and FDR-controlling. In this paper, the main goal is to separate the alternative cases from the null cases. When the number of true alternative hypotheses increases, these controlling procedures become too conservative. Benjamini and Hochberg (2000) proposed an adaptive BH procedure that can improve the decline in the proportion of true null hypotheses of the problem, but ignores the increased expected number of type I errors. Furthermore, a design of data-reformed is proposed and is shown to effectively alleviate the problem caused by data dependencies within the structure. Four different adaptive BH procedures under various scenarios are simulated. The results can serve as a reference for further study.
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