IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v73y2017i2p603-614.html
   My bibliography  Save this article

Bayesian variable selection for post-analytic interrogation of susceptibility loci

Author

Listed:
  • Siying Chen
  • Sara Nunez
  • Muredach P. Reilly
  • Andrea S. Foulkes

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:2:p:603-614
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12620
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lin Zhang & Veerabhadran Baladandayuthapani & Bani K. Mallick & Ganiraju C. Manyam & Patricia A. Thompson & Melissa L. Bondy & Kim-Anh Do, 2014. "Bayesian hierarchical structured variable selection methods with application to molecular inversion probe studies in breast cancer," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(4), pages 595-620, August.
    2. Li, Fan & Zhang, Nancy R., 2010. "Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1202-1214.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    2. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
    3. Aijun Yang & Xuejun Jiang & Lianjie Shu & Jinguan Lin, 2017. "Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis," Computational Statistics, Springer, vol. 32(1), pages 127-143, March.
    4. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    5. Alberto Cassese & Michele Guindani & Philipp Antczak & Francesco Falciani & Marina Vannucci, 2015. "A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants," Biometrics, The International Biometric Society, vol. 71(3), pages 803-811, September.
    6. Nadja Klein & Michael Stanley Smith, 2021. "Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach," Biometrics, The International Biometric Society, vol. 77(3), pages 809-823, September.
    7. 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.
    8. Zhao, Kaifeng & Lian, Heng, 2016. "The Expectation–Maximization approach for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 1-11.
    9. Wang, Xiaoqing & Feng, Xiangnan & Song, Xinyuan, 2020. "Joint analysis of semicontinuous data with latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    10. Zhixiang Lin & Tao Wang & Can Yang & Hongyu Zhao, 2017. "On joint estimation of Gaussian graphical models for spatial and temporal data," Biometrics, The International Biometric Society, vol. 73(3), pages 769-779, September.
    11. Liangliang Zhang & Yushu Shi & Robert R. Jenq & Kim‐Anh Do & Christine B. Peterson, 2021. "Bayesian compositional regression with structured priors for microbiome feature selection," Biometrics, The International Biometric Society, vol. 77(3), pages 824-838, September.
    12. Sunkyung Kim & Wei Pan & Xiaotong Shen, 2013. "Network-Based Penalized Regression With Application to Genomic Data," Biometrics, The International Biometric Society, vol. 69(3), pages 582-593, September.
    13. Jade Xiaoqing Wang & Yimei Li & Wilburn E. Reddick & Heather M. Conklin & John O. Glass & Arzu Onar‐Thomas & Amar Gajjar & Cheng Cheng & Zhao‐Hua Lu, 2023. "A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2430-2443, September.
    14. Bradley W. McEvoy & Rajesh R. Nandy & Ram C. Tiwari, 2013. "Bayesian Approach for Clinical Trial Safety Data Using an Ising Prior," Biometrics, The International Biometric Society, vol. 69(3), pages 661-672, September.
    15. 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.
    16. Kyu Ha Lee & Mahlet G. Tadesse & Andrea A. Baccarelli & Joel Schwartz & Brent A. Coull, 2017. "Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation," Biometrics, The International Biometric Society, vol. 73(1), pages 232-241, March.
    17. 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.
    18. Elin Shaddox & Francesco C. Stingo & Christine B. Peterson & Sean Jacobson & Charmion Cruickshank-Quinn & Katerina Kechris & Russell Bowler & Marina Vannucci, 2018. "A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 59-85, April.
    19. 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.
    20. Chakraborty, Sounak & Lozano, Aurelie C., 2019. "A graph Laplacian prior for Bayesian variable selection and grouping," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 72-91.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:73:y:2017:i:2:p:603-614. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.