Hidden Markov model in multiple testing on dependent count data

References

• Efron B. Correlation and large-scale simultaneous significance testing. J Am Stat Assoc. 2007;102(477):93–103.
   Web of Science ®Google Scholar
• Sun W, Cai TT. Large-scale multiple testing under dependence. J R Stat Soc Ser B (Stat Method). 2009;71(2):393–424.
   Google Scholar
• Scharwartzman A, Lin X. The effect of correlation on false discovery rate estimation. Biometrika. 2011;98(1):199–214.
   PubMed Web of Science ®Google Scholar
• Owen AB. Variance of the number of false discoveries. J R Stat Soc Ser B (Stat Method). 2005;67(3):411–426.
   Google Scholar
• Benjamini Y, Heller R. False discovery rates for spatial signals. J Am Stat Assoc. 2007;102(480):1272–1281.
   Web of Science ®Google Scholar
• Finner H, Dickhaus T, Roters M. Dependency and false discovery rate: asymptotics. Ann Stat. 2007;35(4):1432–1455.
   Web of Science ®Google Scholar
• Heller R. Comment: correlated z-values and the accuracy of large-scale statistical estimates. J Am Stat Assoc. 2010;105(491):1057–1059.
   Web of Science ®Google Scholar
• Guo W, He L, Sarkar SK. Further results on controlling the false discovery proportion. Ann Stat. 2014;42(3):1070–1101.
   Web of Science ®Google Scholar
• Sun W, Reich BJ, Cai TT, et al. False discovery control in large-scale spatial multiple testing. J R Stat Soc Ser B (Stat Method). 2015;77(1):59–83.
   Google Scholar
• Risser MD, Paciorek CJ, Stone DA. Spatially dependent multiple testing under model misspecification, with application to detection of anthropogenic influence on extreme climate events. J Am Stat Assoc. 2019;114(525):61–78.
   Google Scholar
• Wang X, Shojaie A, Zou J. Bayesian hidden Markov models for dependent large-scale multiple testing. Comput Stat Data Anal. 2019;136(C):123–136, 01.
   PubMedGoogle Scholar
• Wei Z, Sun W, Wang K, et al. Multiple testing in genome-wide association studies via hidden Markov models. Bioinformatics. 2009;25(21):2802–2808.
   PubMed Web of Science ®Google Scholar
• Li H, Wei Z, Maris J. A hidden Markov random field model for genome-wide association studies. Biostatistics. 2010;11(1):139–150.
   PubMed Web of Science ®Google Scholar
• Liu J, Zhang C, McCarty C, et al. Graphical-model based multiple testing under dependence, with applications to genome-wide association studies. ArXiv e-prints. 2012; ArXiv1210.4868L.
   Google Scholar
• Shu H, Nan B, Koeppe R. Multiple testing for neuroimaging via hidden Markov random field. Biometrics. 2015;71(3):741–750.
   PubMed Web of Science ®Google Scholar
• Albert PS. A two-state Markov mixture model for a time series of epileptic seizure counts. Biometrics. 1991;47(4):1371–1381.
   PubMed Web of Science ®Google Scholar
• Leroux BG, Puterman ML. Maximum-penalized-likelihood estimation for independent and Markov- dependent mixture models. Biometrics. 1992;48(2):545–558.
   PubMed Web of Science ®Google Scholar
• Orfanogiannaki K, Karlis D, Papadopoulos GA. Identifying seismicity levels via Poisson hidden Markov models. (report). Pure Appl Geophys. 2010;167(8–9):919 (13).
   Web of Science ®Google Scholar
• Lu J, Bushel P. Dynamic expression of 3 ' utrs revealed by Poisson hidden Markov modeling of rna-seq: implications in gene expression profiling. Gene. 2013;07:527.
   Google Scholar
• Rossi L, Chakareski J, Frossard P, et al. A Poisson hidden Markov model for multiview video traffic. IEEE/ACM Trans Netw. 2015;23(2):547–558.
   Web of Science ®Google Scholar
• Gelfand AE, Dey DK, Chang H. Model determination using predictive distributions with implementation via sampling-based methods. In: Bernardo JM, Berger JO, Dawid AP, Smith AFM, editors. Bayesian statistics 4. Oxford: Oxford University Press; 1992. p. 147–167.
   Google Scholar
• Geweke J. Using simulation methods for Bayesian econometric models: inference, development, and communication. Econom Rev. 1999;18(1):1–73.
   Google Scholar
• Frühwirth-Schnatter S. Finite mixture and Markov switching models. New York: Springer; 2006.
   Google Scholar
• Frühwirth-Schnatter S. Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques. Econ J. 2004;7:143–167, 02.
   Google Scholar
• Gelfand AE, Dey DK. Bayesian model choice: asymptotics and exact calculations. J R Stat Soc Ser B (Method). 1994;56(3):501–514.
   Google Scholar
• Newton M, Raftery A. Approximate Bayesian inference by the weighted likelihood bootstrap. J R Stat Soc, Ser B. 1994;56:3–48.
   Google Scholar
• Meng XL, Wong WH. Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Stat Sin. 1996;6(4):831–860.
   Web of Science ®Google Scholar
• Robert CP, Celeux G, Diebolt J. Bayesian estimation of hidden Markov chains: a stochastic implementation. Stat Probab Lett. 1993;16:77–83.
   Web of Science ®Google Scholar
• Albert JH, Chib S. Bayesian analysis of binary and polychotomous response data. J Am Stat Assoc. 1993;88:669–679.
   Web of Science ®Google Scholar
• McCulloch RE, Tsay RS. Statistical analysis of economic time series via Markov switching models. J Time Ser Anal. 1994;15:523–539.
   Google Scholar
• Roeder K, Wasserman L. Practical Bayesian density estimation using mixtures of normals. J Am Stat Assoc. 1997;92(439):894–902.
   Web of Science ®Google Scholar
• Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90(430):773–795.
   Web of Science ®Google Scholar
• Center OS. Ohio supercomputer center. 1987; http://osc.edu/ark:/19495/f5s1ph73.
   Google Scholar
• Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Method). 1995;57(1):289–300.
   Google Scholar
• Storey JD. A direct approach to false discovery rates. J R Stat Soc, Ser B. 2002;64(3):479–498.
   Google Scholar
• Dua D, Graff C. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
   Google Scholar
• Ihler AT, Hutchins J, Smyth P. Adaptive event detection with time-varying Poisson processes. KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006;207–216.
   Google Scholar