EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 18 (2014) ESAIM: PS, 18 (2014) 584-612 References
Free Access
Issue
ESAIM: PS
Volume 18, 2014
Page(s) 584 - 612
DOI https://doi.org/10.1051/ps/2013041
Published online 15 October 2014
  1. D.B. Allison, G.L. Gadbury, M. Heo, J.R. Fernández, C.-K. Lee, T.A. Prolla and R. Weindruch, A mixture model approach for the analysis of microarray gene expression data. Comput. Stat. Data Anal. 39 (2002) 1–20. [CrossRef] [Google Scholar]
  2. J. Aubert, A. Bar-Hen, J.-J. Daudin and S. Robin, Determination of the differentially expressed genes in microarray experiments using local fdr. BMC Bioinformatics 5 (2004) 125. [CrossRef] [PubMed] [Google Scholar]
  3. Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57 (1995) 289–300. [Google Scholar]
  4. A. Celisse, and S. Robin, A cross-validation based estimation of the proportion of true null hypotheses. J. Statist. Plann. Inference 140 (2010) 3132–3147. [CrossRef] [MathSciNet] [Google Scholar]
  5. A.P. Dempster, N.M. Laird and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39 (1977) 1–38. [Google Scholar]
  6. B. Efron, R. Tibshirani, J.D. Storey and V. Tusher, Empirical Bayes analysis of a microarray experiment. J. Amer. Statist. Assoc. 96 (2001) 1151–1160. [CrossRef] [MathSciNet] [Google Scholar]
  7. P. Eggermont and V. LaRiccia, Maximum smoothed likelihood density estimation for inverse problems. Ann. Statist. 23 (1995) 199–220. [CrossRef] [MathSciNet] [Google Scholar]
  8. P. Eggermont and V. LaRiccia, Maximum penalized likelihood estimation. Vol. 1: Density estimation. Springer Ser. Statist. Springer, New York (2001). [Google Scholar]
  9. P.P.B. Eggermont, Nonlinear smoothing and the EM algorithm for positive integral equations of the first kind. Appl. Math. Optim. 39 (1999) 75–91. [CrossRef] [MathSciNet] [Google Scholar]
  10. M. Guedj, S. Robin, A. Celisse and G. Nuel, Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation. BMC Bioinformatics 10 (2009) 84. [CrossRef] [PubMed] [Google Scholar]
  11. M. Langaas, B.H. Lindqvist and E. Ferkingstad, Estimating the proportion of true null hypotheses, with application to DNA microarray data. J.R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 555–572. [Google Scholar]
  12. M. Levine, D.R. Hunter and D. Chauveau, Maximum smoothed likelihood for multivariate mixtures. Biometrika 98 (2011) 403–416. [CrossRef] [Google Scholar]
  13. J. Liao, Y. Lin, Z.E. Selvanayagam and W.J. Shih, A mixture model for estimating the local false discovery rate in DNA microarray analysis. Bioinformatics 20 (2004) 2694–2701. [CrossRef] [PubMed] [Google Scholar]
  14. G. McLachlan, R. Bean and L.B.-T. Jones, A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22 (2006) 1608–1615. [CrossRef] [PubMed] [Google Scholar]
  15. P. Neuvial, Intrinsic bounds and false discovery rate control in multiple testing problems. Technical report (2010). arXiv:1003.0747. [Google Scholar]
  16. V. Nguyen and C. Matias, On efficient estimators of the proportion of true null hypotheses in a multiple testing setup. Technical report (2012). Preprint arXiv:1205.4097. [Google Scholar]
  17. S. Pounds and S.W. Morris, Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19 (2003) 1236–1242. [CrossRef] [PubMed] [Google Scholar]
  18. S. Robin, A. Bar-Hen, J.-J. Daudin and L. Pierre, A semi-parametric approach for mixture models: application to local false discovery rate estimation. Comput. Statist. Data Anal. 51 (2007) 5483–5493. [CrossRef] [MathSciNet] [Google Scholar]
  19. T. Schweder, and E. Spjøtvoll, Plots of p-values to evaluate many tests simultaneously. Biometrika 69 (1982) 493–502. [CrossRef] [Google Scholar]
  20. B.W. Silverman, Density estimation for statistics and data analysis. Monogr. Statist. Appl. Prob. Chapman & Hall, London (1986). [Google Scholar]
  21. J.D. Storey, A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B Stat. Methodol. 64 (2002) 479–498. [Google Scholar]
  22. J.D. Storey, The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Statist. 31 (2003) 2013–2035. [Google Scholar]
  23. K. Strimmer, A unified approach to false discovery rate estimation. BMC Bioinformatics 9 (2008) 303. [CrossRef] [PubMed] [Google Scholar]
  24. W. Sun and T. Cai, Oracle and adaptive compound decision rules for false discovery rate control. J. Am. Stat. Assoc. 102 (2007) 901–912. [CrossRef] [Google Scholar]
  25. W. Sun and T. Cai, Large-scale multiple testing under dependence. J. Royal Stat. Soc. Series B (Statistical Methodology) 71 (2009) 393–424. [CrossRef] [Google Scholar]
  26. A.B. Tsybakov, Introduction to nonparametric estimation. Springer Ser. Statist. Springer, New York (2009). [Google Scholar]
  27. D. Wied and R. Weißbach, Consistency of the kernel density estimator: a survey. Stat. Papers 53 (2012) 1–21. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.