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研究生: 張宗楷
Chung-Kai Chang
論文名稱: 改良型均差法
Adjusted MD method
指導教授: 謝文萍
Wen-Ping Hsieh
口試委員:
學位類別: 碩士
Master
系所名稱: 理學院 - 統計學研究所
Institute of Statistics
論文出版年: 2006
畢業學年度: 94
語文別: 中文
論文頁數: 37
中文關鍵詞: 虛無假設均差法改良
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  • 多重比較在微陣列技術上面的應用是很熱門的課題,為了能精準控制誤判率,通常採用的輔助方法是估計虛無假設的數目,再做進一步的計算。在這個領域上面,MD法有著不錯的表現,但是有些領域的研究需要更大量的基因,MD法的估計則會略顯保守,在這裡我們以改變程序起始點的方式對MD法做了一些修正,這個修正還可以選擇你可以接受的誤差的估計值。


    Abstract
    The Multiple test applied on microarray is still a hot issue. For precision estimation, a well-accepted method is to estimate the number of null hypotheses. In this topic MD method performs usefully. But the researcher may need more genes for his study. We build a adjusted method for this request. We change the starting location of the original method and plot a curve to choose the number depending on the standard deviation you can accept.

    Table of contents 1 Introduction...........................................1 2 MD method..............................................4 2.1 Notation............................................4 2.2 LSL method..........................................4 2.3 MD method...........................................5 3 The first jump issue...................................6 4 Asymptotic properties of MD method and modification....9 4.1 Simulation with truncated exponential distribution..9 4.2 Adjusted step-down MD..............................11 4.3 Adjusted step-up MD method.........................20 5 Real data analysis....................................28 6 Reference.............................................33 7 Appendix..............................................35

    Allison, D. B., Gadbury, G. L., Heo, M., Fernandez, J. R., Lee, C. K., Prolla, T. A. and Weindruck, R. (2002) A mixture model approach for the analysis of microarray gene expression data. Computational Statistics & Data Anal., 39, 1-20.

    Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate:a practical and powerful approach to multiple testing. J. R. Statist. Soc. B, 57, 289-300.

    Benjamini, Y. and Hochberg, Y. (2000) On the adaptive control of the false discovery rate in multiple hypothesis testing with independent statistics. J. Educational & Behavioral Statist. , 25, 60-83.

    Broberg, P. (2004) A new estimate of the proportion unchanged genes in a microarray experiment. Genome Biology ,5:p10

    Efron, B., Tibshirani, R., Storey, J. D. and Tusher, V. (2001) Empirical Bayes analysis of a microarray experiment. J. Am. Statist. Ass. , 96, 1151-1160.

    Hochberg, Y., Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine 9, 811-818.

    Hochberg, Y., Tamhane, A. C. (1987). Multiple Comparison Procedures. New York. John Wiley & Sons.

    Hsueh, H., Chen, J. J., Kodell, R. L. (2003) Comparison of methods for estimating the number of true null hypothesis in multiplicity testing.
    J. Biopharmaceutical Statist. 13, 675-689.

    Genovese, C. R. and Wasserman, L. (2002) Operating characteristics and extensions of the FDR procedure. J. R. Stat. Soc. Ser. B. 64, 499-518.

    Genovese, C. and Wasserman, L. (2002) A Large-sample approach to False discovery rates controlling. Technical Report. Department of Statistics, Carnegie Mellon University, Pittsburgh.

    Genovese, C. R. and Wasserman, L. (2004) A stochastic process approach to false discovery control. Annals of Statistics, 32(3), 1035-1061.

    Storey, J.D. (2002) A direct approach to false discovery rates. J. R. Statist. Soc. B, 64, 479-498.

    Storey, J.D. (2003) Statistical significance for genomewide studies. PNAS. 100, 9440-9445

    Storey, J. D., Taylor, J. E. & Siegmund, D. (2004). Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: a unified approach. J. R. Statist. Soc., Ser. B, 66, 187-205.

    Tsai, C. A., Hsueh, H., Chen, J.J.(2003) Estimation of False Discovery Rates in Multiple Testing: Application to Gene Microarray Data. Biometrics, 59, 1071-1081.

    Westfall, P. H., Young, S. S. (1993). Resampling-Based Multiple Testing. New York: John Wiley & Sons.

    Yang, J. J. and Yang, M. (2006) An improved procedure for gene selection from microarray experiments using false discovery rate criterion. BMC Bioinformatics. 2006, 7:15,

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