Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models
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DOI: 10.1007/s11749-020-00746-8
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- Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
- Wensheng Zhu & Yuan Jiang & Heping Zhang, 2012. "Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall's Tau," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 1-11, March.
- Wenguang Sun & Brian J. Reich & T. Tony Cai & Michele Guindani & Armin Schwartzman, 2015. "False discovery control in large-scale spatial multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 59-83, January.
- T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
- Sun, Wenguang & Cai, T. Tony, 2007. "Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 901-912, September.
- Liang, Kun & Nettleton, Dan, 2010. "A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1444-1454.
- Zhang, Heping & Liu, Ching-Ti & Wang, Xueqin, 2010. "An Association Test for Multiple Traits Based on the Generalized Kendall’s Tau," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 473-481.
- Lihua Lei & William Fithian, 2018. "AdaPT: an interactive procedure for multiple testing with side information," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 649-679, September.
- Hai Shu & Bin Nan & Robert Koeppe, 2015. "Multiple testing for neuroimaging via hidden Markov random field," Biometrics, The International Biometric Society, vol. 71(3), pages 741-750, September.
- Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
- Wenguang Sun & T. Tony Cai, 2009. "Large‐scale multiple testing under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 393-424, April.
- Pei Fen Kuan & Derek Y. Chiang, 2012. "Integrating Prior Knowledge in Multiple Testing under Dependence with Applications to Detecting Differential DNA Methylation," Biometrics, The International Biometric Society, vol. 68(3), pages 774-783, September.
- Christopher Genovese & Larry Wasserman, 2002. "Operating characteristics and extensions of the false discovery rate procedure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 499-517, August.
- John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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Cited by:
- Wang, Jiangzhou & Cui, Tingting & Zhu, Wensheng & Wang, Pengfei, 2023. "Covariate-modulated large-scale multiple testing under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
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Keywords
Factorial hidden Markov model; Covariate adjustment; Multiple hypotheses testing; False discovery rate; GWAS;All these keywords.
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