IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v73y2017i1p232-241.html

Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation

Author

Listed:
  • Kyu Ha Lee
  • Mahlet G. Tadesse
  • Andrea A. Baccarelli
  • Joel Schwartz
  • Brent A. Coull

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:232-241
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12557
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    2. repec:dau:papers:123456789/4911 is not listed on IDEAS
    3. 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.
    4. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anna Freni-Sterrantino & Denis Rustand & Janet van Niekerk & Elias Teixeira Krainski & Håvard Rue, 2025. "A graphical framework for interpretable correlation matrix models for multivariate regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(3), pages 409-447, July.

    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. Qin, Shanshan & Zhang, Guanlin & Wu, Yuehua & Zhu, Zhongyi, 2025. "Bayesian grouping-Gibbs sampling estimation of high-dimensional linear model with non-sparsity," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    3. 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.
    4. Zhao, Kaifeng & Lian, Heng, 2016. "The Expectation–Maximization approach for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 1-11.
    5. 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.
    6. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    7. Camarero, Mariam & Forte, Anabel & Garcia-Donato, Gonzalo & Mendoza, Yurena & Ordoñez, Javier, 2015. "Variable selection in the analysis of energy consumption–growth nexus," Energy Economics, Elsevier, vol. 52(PA), pages 207-216.
    8. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2011. "Market Freedom and the Global Recession," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(1), pages 111-135, April.
    9. M. J. Bayarri & G. García‐Donato, 2008. "Generalization of Jeffreys divergence‐based priors for Bayesian hypothesis testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 981-1003, November.
    10. Gravina, Antonio Francesco & Lanzafame, Matteo, 2025. "‘What's your shape?’ A data-driven approach to estimating the Environmental Kuznets Curve," Energy Economics, Elsevier, vol. 148(C).
    11. Frederico Finan & Demian Pouzo, 2026. "Learning about Treatment Effects with Prior Studies: A Bayesian Model Averaging Approach," Papers 2601.09888, arXiv.org, revised Mar 2026.
    12. Branimir Jovanovic, 2012. "How Policy Actions Affect Short-term Post-crisis Recovery?," CEIS Research Paper 253, Tor Vergata University, CEIS, revised 05 Oct 2012.
    13. Jewson Stephen & Penzer Jeremy, 2006. "Estimating Trends in Weather Series: Consequences for Pricing Derivatives," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-17, September.
    14. Luebke, Karsten & Czogiel, Irina & Weihs, Claus, 2004. "Latent Factor Prediction Pursuit for Rank Deficient Regressors," Technical Reports 2004,75, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    15. Dimitris Fouskakis & Ioannis Ntzoufras, 2017. "Information consistency of the Jeffreys power-expected-posterior prior in Gaussian linear models," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 371-380, December.
    16. Alfredo Altuzarra & Pilar Gargallo & José María Moreno-Jiménez & Manuel Salvador, 2022. "Identification of Homogeneous Groups of Actors in a Local AHP-Multiactor Context with a High Number of Decision-Makers: A Bayesian Stochastic Search," Mathematics, MDPI, vol. 10(3), pages 1-20, February.
    17. Mulder, Joris, 2014. "Prior adjusted default Bayes factors for testing (in)equality constrained hypotheses," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 448-463.
    18. repec:osf:socarx:n49hv_v1 is not listed on IDEAS
    19. Srivastava, M. S. & Kubokawa, T., 2005. "Minimax multivariate empirical Bayes estimators under multicollinearity," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 394-416, April.
    20. Ons Jedidi & Jean Sébastien Pentecote, 2015. "Robust Signals for Banking Crises," Economics Bulletin, AccessEcon, vol. 35(3), pages 1617-1629.
    21. Anna Sokolova, 2023. "Marginal Propensity to Consume and Unemployment: a Meta-analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 813-846, December.

    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:1:p:232-241. 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.