References

1. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57, 289–300. [Crossref], [Web of Science ®], [Google Scholar]
2. Benjamini, Y., & Yekutieli, (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188. [Google Scholar]
3. Brown, A., Lazar, N., Datta, G., Jang, W., & McDowell, J. (2014). Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging. Neuroimage, 84, 97–112. [Google Scholar]
4. Efron, B. (2007). Correlation and large-scale simultaneous significance testing. Journal of the American Statistical Association, 102, 93–103. [Taylor & Francis Online], [Google Scholar]
5. Efron, B. (2012). Large-scale inference: Empirical Bayes methods for estimation, testing and prediction. Cambridge, UK: Cambridge University Press. [Google Scholar]
6. Efron, B., Tibshirani, R., Storey, J., & Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96, 1151–1160. [Taylor & Francis Online], [Google Scholar]
7. Farcomeni, A. (2007). Some results on the control of the false discovery rate under dependence. Scandinavian Journal of Statistics, 34, 275–297. [Google Scholar]
8. Illston, B., Basara, J., Fisher, D., Elliott, R., Fiebrich, C., Crawford, K.,… Hunt, E. (2008). Mesoscale monitoring of soil moisture across a statewide network. Journal of Atmospheric and Oceanic Technology, 25, 167–182. [Google Scholar]
9. Liang, F., Paulo, R., Molina, G., Clyde, M., & Berger, J. (2008). Mixtures of g priors for Bayesian variable selection. Journal of the American Statistical Association, 103, 410–423. [Taylor & Francis Online], [Google Scholar]
10. Liang, K., & Nettleton, D. (2012). Adaptive and dynamic adaptive procedures for false discovery rate control and estimation. Journal of the Royal Statistical Society, Series B, 74, 163–182. [Google Scholar]
11. Muller, P., Parmigiani, G., & Rice, K. (2006). FDR and Bayesian multiple comparisons rules. Proceedings of Valencia/ISBA 8th world meeting on Bayesian statistics, Benidorm, Alicante, Spain. [Google Scholar]
12. Scott, J., & Berger, J. (2006). An exploration of aspects of Bayesian multiple testing. Journal of Statistical Planning and Inference, 136, 2144–2162. [Google Scholar]
13. Scott, J., & Berger, J. (2010). Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem. The Annals of Statistics, 38, 2587–2619. [Google Scholar]
14. Storey, J., Taylor, J., & Siegmund, D. (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, 66, 187–205. [Google Scholar]
15. Sun, W., & Cai, T. (2009). Large-scale multiple testing under dependence. Journal of the Royal Statistical Society, Series B, 71, 393–424. [Google Scholar]
16. Sun, W., Reich, B., Cai, T., Guindani, M., & Schwartzman, A. (2015). False discovery control in large-scale spatial multiple testing. Journal of the Royal Statistical Society, Series B, 77, 59–83. [Google Scholar]
17. Wu, W. (2008). On false discovery control under dependence. Annals of Statistics, 36, 364–380. [Google Scholar]