IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v103y2008mjunep534-546.html
   My bibliography  Save this article

Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes

Author

Listed:
  • Dunson, David B.
  • Herring, Amy H.
  • Engel, Stephanie M.

Abstract

No abstract is available for this item.

Suggested Citation

  • Dunson, David B. & Herring, Amy H. & Engel, Stephanie M., 2008. "Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 534-546, June.
  • Handle: RePEc:bes:jnlasa:v:103:y:2008:m:june:p:534-546
    as

    Download full text from publisher

    File URL: http://www.ingentaconnect.com/content/asa/jasa/2008/00000103/00000482/art00012
    File Function: full text
    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.

    Citations

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


    Cited by:

    1. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    2. Korobilis, Dimitris, 2013. "Bayesian forecasting with highly correlated predictors," Economics Letters, Elsevier, vol. 118(1), pages 148-150.
    3. repec:ipg:wpaper:2014-465 is not listed on IDEAS
    4. Glen McGee & Ander Wilson & Thomas F. Webster & Brent A. Coull, 2023. "Bayesian multiple index models for environmental mixtures," Biometrics, The International Biometric Society, vol. 79(1), pages 462-474, March.
    5. 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.
    6. Richard F. MacLehose & David B. Dunson, 2010. "Bayesian Semiparametric Multiple Shrinkage," Biometrics, The International Biometric Society, vol. 66(2), pages 455-462, June.
    7. Jessie J Hsu & Dianne M Finkelstein & David A Schoenfeld, 2015. "Outcome-Driven Cluster Analysis with Application to Microarray Data," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-15, November.
    8. Zarepour, Mahmoud & Labadi, Luai Al, 2012. "On a rapid simulation of the Dirichlet process," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 916-924.
    9. Lauren Hoskovec & Wande Benka-Coker & Rachel Severson & Sheryl Magzamen & Ander Wilson, 2021. "Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
    10. Lee, Kuo-Jung & Feldkircher, Martin & Chen, Yi-Chi, 2021. "Variable selection in finite mixture of regression models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    11. Paul Hofmarcher & Jesús Crespo Cuaresma & Bettina Grün & Kurt Hornik, 2015. "Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 133-144, March.
    12. Christou, Christina & Gupta, Rangan & Hassapis, Christis, 2017. "Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 50-60.
    13. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2017. "A Semiparametric Bayesian Approach to a New Dynamic Zero-Inflated Model," Working Papers 2017001, The University of Sheffield, Department of Economics.
    14. Korobilis, Dimitris, 2016. "Prior selection for panel vector autoregressions," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 110-120.
    15. Barrientos, Andrés F. & Canale, Antonio, 2021. "A Bayesian goodness-of-fit test for regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    16. Alain Kabundi & Eliphas Ndou & Nombulelo Gumata, 2013. "Important Channels of Transmission Monetary Policy Shock in South Africa," Working Papers 375, Economic Research Southern Africa.
    17. Howard D. Bondell & Brian J. Reich, 2012. "Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1610-1624, December.
    18. Naik, Prasad A., 2015. "Marketing Dynamics: A Primer on Estimation and Control," Foundations and Trends(R) in Marketing, now publishers, vol. 9(3), pages 175-266, December.
    19. Nicholas Apergis & Ghassen El Montasser & Emmanuel Owusu-Sekyere & Ahdi N. Ajmi & Rangan Gupta, 2014. "Dutch Disease Effect of Oil Rents on Agriculture Value Added in MENA Countries," Working Papers 201408, University of Pretoria, Department of Economics.
    20. Korobilis, Dimitris, 2015. "Prior selection for panel vector autoregressions," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-73, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    21. Subharup Guha & Rex Jung & David Dunson, 2022. "Predicting phenotypes from brain connection structure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 639-668, June.
    22. Canale, Antonio & Lijoi, Antonio & Nipoti, Bernardo & Prünster, Igor, 2023. "Inner spike and slab Bayesian nonparametric models," Econometrics and Statistics, Elsevier, vol. 27(C), pages 120-135.
    23. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "Heterogeneous variable selection in nonlinear panel data models: A semiparametric Bayesian approach," Tinbergen Institute Discussion Papers 20-061/III, Tinbergen Institute.
    24. Han, Shengtong & Zhang, Hongmei & Karmaus, Wilfried & Roberts, Graham & Arshad, Hasan, 2017. "Adjusting background noise in cluster analyses of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 93-104.

    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:bes:jnlasa:v:103:y:2008:m:june:p:534-546. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    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.