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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/18174
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/18174

    Title: Comparison of methods for estimating the number of true null hypotheses in mulitplicity testing
    Authors: Hsueh, Huey-Miin;Chen J. J.;Kodel R. L.
    Date: 2003
    Issue Date: 2008-12-19 14:52:55 (UTC+8)
    Abstract: When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.
    Relation: Journal of Biopharmaceutical Statistics, 13(4),675-689
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1081/BIP-120024202
    DOI: 10.1081/BIP-120024202
    Appears in Collections:[統計學系] 期刊論文

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