422 related articles for article (PubMed ID: 14584715)
1. Comparison of methods for estimating the number of true null hypotheses in multiplicity testing.
Hsueh HM; Chen JJ; Kodell RL
J Biopharm Stat; 2003 Nov; 13(4):675-89. PubMed ID: 14584715
[TBL] [Abstract][Full Text] [Related]
2. Constructing multiple test procedures for partially ordered hypothesis sets.
Edwards D; Madsen J
Stat Med; 2007 Dec; 26(28):5116-24. PubMed ID: 17476650
[TBL] [Abstract][Full Text] [Related]
3. Wavelet thresholding with bayesian false discovery rate control.
Tadesse MG; Ibrahim JG; Vannucci M; Gentleman R
Biometrics; 2005 Mar; 61(1):25-35. PubMed ID: 15737075
[TBL] [Abstract][Full Text] [Related]
4. Empirical Bayes screening of many p-values with applications to microarray studies.
Datta S; Datta S
Bioinformatics; 2005 May; 21(9):1987-94. PubMed ID: 15691856
[TBL] [Abstract][Full Text] [Related]
5. Power and sample size when multiple endpoints are considered.
Senn S; Bretz F
Pharm Stat; 2007; 6(3):161-70. PubMed ID: 17674404
[TBL] [Abstract][Full Text] [Related]
6. False discovery rate and permutation test: an evaluation in ERP data analysis.
Lage-Castellanos A; Martínez-Montes E; Hernández-Cabrera JA; Galán L
Stat Med; 2010 Jan; 29(1):63-74. PubMed ID: 19941298
[TBL] [Abstract][Full Text] [Related]
7. Multiple test procedures using an upper bound of the number of true hypotheses and their use for evaluating high-dimensional EEG data.
Hemmelmann C; Ziegler A; Guiard V; Weiss S; Walther M; Vollandt R
J Neurosci Methods; 2008 May; 170(1):158-64. PubMed ID: 18279970
[TBL] [Abstract][Full Text] [Related]
8. A method to increase the power of multiple testing procedures through sample splitting.
Rubin D; Dudoit S; van der Laan M
Stat Appl Genet Mol Biol; 2006; 5():Article19. PubMed ID: 17049030
[TBL] [Abstract][Full Text] [Related]
9. Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures.
Lu X; Perkins DL
BMC Bioinformatics; 2007 May; 8():157. PubMed ID: 17509157
[TBL] [Abstract][Full Text] [Related]
10. Cluster formation as a measure of interpretability in multiple testing.
Shaffer JP
Biom J; 2008 Oct; 50(5):852-60. PubMed ID: 18932143
[TBL] [Abstract][Full Text] [Related]
11. A powerful truncated tail strength method for testing multiple null hypotheses in one dataset.
Jiang B; Zhang X; Zuo Y; Kang G
J Theor Biol; 2011 May; 277(1):67-73. PubMed ID: 21295595
[TBL] [Abstract][Full Text] [Related]
12. Comparison of false discovery rate methods in identifying genes with differential expression.
Qian HR; Huang S
Genomics; 2005 Oct; 86(4):495-503. PubMed ID: 16054333
[TBL] [Abstract][Full Text] [Related]
13. Resampling-based empirical Bayes multiple testing procedures for controlling generalized tail probability and expected value error rates: focus on the false discovery rate and simulation study.
Dudoit S; Gilbert HN; van der Laan MJ
Biom J; 2008 Oct; 50(5):716-44. PubMed ID: 18932138
[TBL] [Abstract][Full Text] [Related]
14. Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005. Control of the false discovery rate applied to the detection of positively selected amino acid sites.
Guindon S; Black M; Rodrigo A;
Mol Biol Evol; 2006 May; 23(5):919-26. PubMed ID: 16423864
[TBL] [Abstract][Full Text] [Related]
15. The fallback procedure for evaluating a single family of hypotheses.
Wiens BL; Dmitrienko A
J Biopharm Stat; 2005; 15(6):929-42. PubMed ID: 16279352
[TBL] [Abstract][Full Text] [Related]
16. Screening for partial conjunction hypotheses.
Benjamini Y; Heller R
Biometrics; 2008 Dec; 64(4):1215-22. PubMed ID: 18261164
[TBL] [Abstract][Full Text] [Related]
17. A generalized Sidak-Holm procedure and control of generalized error rates under independence.
Guo W; Romano J
Stat Appl Genet Mol Biol; 2007; 6():Article3. PubMed ID: 17402918
[TBL] [Abstract][Full Text] [Related]
18. Interpretation of research data: hypothesis testing.
Stolar MH
Am J Hosp Pharm; 1980 Nov; 37(11):1539-45. PubMed ID: 7211860
[TBL] [Abstract][Full Text] [Related]
19. An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes.
Lin D; Shkedy Z; Burzykowski T; Ion R; Göhlmann HW; Bondt AD; Perer T; Geerts T; Van den Wyngaert I; Bijnens L
Biom J; 2008 Oct; 50(5):801-23. PubMed ID: 18932139
[TBL] [Abstract][Full Text] [Related]
20. On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure.
Green GH; Diggle PJ
Stat Appl Genet Mol Biol; 2007; 6():Article27. PubMed ID: 18052910
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
