1. Benditkis, J., P. Heesen, and A. Janssen (2018). The false discovery rate (fdr) of multiple tests in a class room lecture. Statistics & Probability Letters, 134, 29-35. [
DOI:10.1016/j.spl.2017.09.017]
2. Benditkis, J. and A. Janssen (2017). Finite sample bounds for expected number of false rejections under martingale dependence with applications to FDR. Electronic Journal of Statistics, 11(1), 1827-1857. [
DOI:10.1214/17-EJS1268]
3. Benjamini, Y. and Y. Hochberg (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B. Methodological, 57(1), 289-300. [
DOI:10.1111/j.2517-6161.1995.tb02031.x]
4. Benjamini, Y. and W. Liu (1999). A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence. Journal of Statistical Planning and Inference, 82(1), 163-170. [
DOI:10.1016/S0378-3758(99)00040-3]
5. Benjamini, Y. and D. Yekutieli (2001). The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics, 29(4), 1165-1188. [
DOI:10.1214/aos/1013699998]
6. Blanchard, G. and É. Roquain (2009). Adaptive false discovery rate control under independence and dependence. Journal of Machine Learning Research, 10(Dec), 2837-2871.
7. Cai, T. T. and W. Sun (2009). Simultaneous testing of grouped hypotheses: Finding needles in multiple haystacks. Journal of the American Statistical Association, 104(488), 1467-1481. [
DOI:10.1198/jasa.2009.tm08415]
8. Heesen, P., A. Janssen, et al. (2015). Inequalities for the false discovery rate (fdr) under dependence. Electronic Journal of Statistics, 9(1), 679-716. [
DOI:10.1214/15-EJS1016]
9. Klenke, A. (2013). Probability Theory: A Comprehensive Course. Universitext. Springer London. [
DOI:10.1007/978-1-4471-5361-0]
10. Li, A. and R. F. Barber (2019). Multiple testing with the structure-adaptive benjamini- hochberg algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(1), 45-74. [
DOI:10.1111/rssb.12298]
11. Peña, E. A., J. D. Habiger, and W. Wu (2011). Power-enhanced multiple decision functions controlling family-wise error and false discovery rates. Annals of statistics, 39(1), 556. [
DOI:10.1214/10-AOS844]
12. Ploner, A., S. Calza, A. Gusnanto, and Y. Pawitan (2006). Multidimensional local false discovery rate for microarray studies. Bioinformatics, 22(5), 556-565. [
DOI:10.1093/bioinformatics/btk013]
13. Ramdas, A., J. Chen, M. J. Wainwright, and M. I. Jordan (2017). Dagger: A sequential algorithm for fdr control on dags. arXiv preprint arXiv:1709.10250.
14. Romano, J. P., A. M. Shaikh, and M. Wolf (2008). Control of the false discovery rate under dependence using the bootstrap and subsampling. Test, 17(3), 417. [
DOI:10.1007/s11749-008-0126-6]
15. Sarkar, S. K. (2002). Some results on false discovery rate in stepwise multiple testing procedures. The Annals of Statistics, 30(1), 239-257. [
DOI:10.1214/aos/1015362192]
16. Storey, J. D., J. E. Taylor, and D. Siegmund (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(1), 187-205. [
DOI:10.1111/j.1467-9868.2004.00439.x]
17. Sun, W. and T. T. Cai (2007). Oracle and adaptive compound decision rules for false discovery rate control. Journal of the American Statistical Association, 102(479), 901-912. [
DOI:10.1198/016214507000000545]
