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Given a nonparametric Hidden Markov Model (HMM) with two states, the question of constructing efficient multiple testing procedures is considered, treating one of the states as an unknown null hypothesis.
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D. Blackwell · 1957
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L. E. Baum and T. Petrie · 1966
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Y. Benjamini and Y. Hochberg · 1995
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A. van der Vaart · 1998
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Y. Benjamini and D. Yekutieli · 2001
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Asymptotics of the maximum likelihood estimator for general hidden Markov models
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The positive false discovery rate: a Bayesian interpretation and the q q -value
J. D. Storey · 2003
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Optimal sample size for multiple testing: the case of gene expression microarrays
P. Müller, G. Parmigiani, C. Robert, and J. Rousseau · 2004
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Introduction to nonparametric estimation
A. B. Tsybakov · 2004
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O. Cappé, E. Moulines, and T. Rydén · 2005
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Some results on the control of the false discovery rate under dependence
A. Farcomeni · 2007
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Dependency and false discovery rate: asymptotics
H. Finner, T. Dickhaus, and M. Roters · 2007
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Nonparametric identification and maximum likelihood estimation for hidden Markov models
G. Alexandrovich, H. Holzmann, and A. Leister · 2016
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Y. De Castro, E. Gassiat, and C. Lacour · 2016
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E. Gassiat, A. Cleynen, and S. Robin · 2016
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Mathematical foundations of infinite-dimensional statistical models
E. Giné and R. Nickl · 2016
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Consistent estimation of the filtering and marginal smoothing distributions in nonparametric hidden Markov models
Y. De Castro, E. Gassiat, and S. Le Corff · 2017
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Optimal control of false discovery criteria in the two-group model
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Hidden Markov model in multiple testing on dependent count data
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