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Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach

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  • Ghosh, Joyee
  • Clyde, Merlise A.

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  • Ghosh, Joyee & Clyde, Merlise A., 2011. "Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1041-1052.
    • Handle: RePEc:bes:jnlasa:v:106:i:495:y:2011:p:1041-1052
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    Cited by:

    1. Chris J. Oates & Mark Girolami & Nicolas Chopin, 2017. "Control functionals for Monte Carlo integration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 695-718, June.
    2. Perrakis, Konstantinos & Ntzoufras, Ioannis & Tsionas, Efthymios G., 2014. "On the use of marginal posteriors in marginal likelihood estimation via importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 54-69.
    3. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    4. Gonzalo García‐Donato & Stefano Cabras & María Eugenia Castellanos, 2023. "Model uncertainty quantification in Cox regression," Biometrics, The International Biometric Society, vol. 79(3), pages 1726-1736, September.
    5. Li Ma, 2015. "Scalable Bayesian Model Averaging Through Local Information Propagation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 795-809, June.
    6. Joyee Ghosh & Andrew E. Ghattas, 2015. "Bayesian Variable Selection Under Collinearity," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 165-173, August.

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