Bayesian hierarchical structured variable selection methods with application to molecular inversion probe studies in breast cancer
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Cited by:
- Jonathan Heydari & Conor Lawless & David A. Lydall & Darren J. Wilkinson, 2016. "Bayesian hierarchical modelling for inferring genetic interactions in yeast," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 367-393, April.
- Mogliani, Matteo & Simoni, Anna, 2021.
"Bayesian MIDAS penalized regressions: Estimation, selection, and prediction,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
- Matteo Mogliani & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.
- Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
- Matteo Mogliani, 2019. "Bayesian MIDAS penalized regressions: estimation, selection, and prediction," Working papers 713, Banque de France.
- Siying Chen & Sara Nunez & Muredach P. Reilly & Andrea S. Foulkes, 2017. "Bayesian variable selection for post-analytic interrogation of susceptibility loci," Biometrics, The International Biometric Society, vol. 73(2), pages 603-614, June.
- Yize Zhao & Ben Wu & Jian Kang, 2023. "Bayesian interaction selection model for multimodal neuroimaging data analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 655-668, June.
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