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Bayesian group selection with non-local priors

Author

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  • Weibing Li

    (University of Minnesota Duluth)

  • Thierry Chekouo

    (University of Calgary)

Abstract

In many applications, variables or features can be naturally partitioned into different groups. In this article, we propose a new Bayesian hierarchical model for group selection problem when the group structure is known. We use spike and slab priors for regression coefficients, and the slab component is assumed to come from the family of nonlocal priors. Contrary to local priors commonly used in Bayesian group selection, nonlocal density priors vanish when a regression coefficient in the model is zero. We use simulation studies to assess the performance of our method and apply it to data collected from individuals undergoing cardiac catheterization at Duke University Medical center between 2001 and 2010.

Suggested Citation

  • Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01115-1
    DOI: 10.1007/s00180-021-01115-1
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    References listed on IDEAS

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    1. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2015. "miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer," Biometrics, The International Biometric Society, vol. 71(2), pages 428-438, June.
    2. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    3. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2017. "A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study," Biometrics, The International Biometric Society, vol. 73(2), pages 615-624, June.
    4. David Rossell & Donatello Telesca, 2017. "Nonlocal Priors for High-Dimensional Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 254-265, January.
    5. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
    6. Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
    7. Thierry Chekouo & Alejandro Murua, 2018. "High-dimensional variable selection with the plaid mixture model for clustering," Computational Statistics, Springer, vol. 33(3), pages 1475-1496, September.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Valen E. Johnson & David Rossell, 2010. "On the use of non‐local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170, March.
    10. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    11. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. Fang Yang & Liangliang Zhang & Jingyi Zheng & Xuan Cao, 2024. "Consistent group selection using nonlocal priors in regression," Statistical Papers, Springer, vol. 65(2), pages 989-1019, April.

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