English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 95905/126495 (76%)
Visitors : 31778915      Online Users : 380
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/72089
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/72089

    Title: Incorporating the number of true null hypotheses to improve power in multiple testing: application to gene microarray data
    Authors: Hsueh, Huey-Miin;Tsai, Chen-An;Chen,James J.
    Contributors: 統計系
    Date: 2006-06
    Issue Date: 2014-12-16 10:39:05 (UTC+8)
    Abstract: Testing for significance with gene expression data from DNA microarray experiments involves simultaneous comparisons of hundreds or thousands of genes. In common exploratory microarray experiments, most genes are not expected to be differentially expressed. The family-wise error (FWE) rate and false discovery rate (FDR) are two common approaches used to account for multiple hypothesis tests to identify differentially expressed genes. When the number of hypotheses is very large and some null hypotheses are expected to be true, the power of an FWE or FDR procedure can be improved if the number of null hypotheses is known. The mean of differences (MD) of ranked p-values has been proposed to estimate the number of true null hypotheses under the independence model. This article proposes to incorporate the MD estimate into an FWE or FDR approach for gene identification. Simulation results show that the procedure appears to control the FWE and FDR well at the FWE=0.05 and FDR=0.05 significant levels; it exceeds the nominal level for FDR=0.01 when the null hypotheses are highly correlated, a correlation of 0.941. The proposed approach is applied to a public colon tumor data set for illustration.
    Relation: Journal of Statistical Computation and Simulation,77(9),757-767.
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1080/10629360600648651
    DOI: 10.1080/10629360600648651
    Appears in Collections:[統計學系] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback