IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v6y2010i1n33.html
   My bibliography  Save this article

Two-Level Stochastic Search Variable Selection in GLMs with Missing Predictors

Author

Listed:
  • Mitra Robin

    (Southampton Statistical Sciences Research Institute)

  • Dunson David

    (Duke University)

Abstract

Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

Suggested Citation

  • Mitra Robin & Dunson David, 2010. "Two-Level Stochastic Search Variable Selection in GLMs with Missing Predictors," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-40, October.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:1:n:33
    DOI: 10.2202/1557-4679.1173
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1173
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Satkartar K. Kinney & David B. Dunson, 2007. "Fixed and Random Effects Selection in Linear and Logistic Models," Biometrics, The International Biometric Society, vol. 63(3), pages 690-698, September.
    2. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, January.
    3. 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.
    4. Bo Cai & David B. Dunson, 2006. "Bayesian Covariance Selection in Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 62(2), pages 446-457, June.
    5. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
    6. Cottet, Remy & Kohn, Robert J. & Nott, David J., 2008. "Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 661-671, June.
    7. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
    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. Joseph Antonelli & Georgia Papadogeorgou & Francesca Dominici, 2022. "Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties," Biometrics, The International Biometric Society, vol. 78(1), pages 100-114, March.
    2. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.

    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. Zhongqi Liang & Qihua Wang & Yuting Wei, 2022. "Robust model selection with covariables missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 539-557, June.
    2. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
    3. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    4. Katrin Wölfel & Christoph S. Weber, 2017. "Searching for the Fed’s reaction function," Empirical Economics, Springer, vol. 52(1), pages 191-227, February.
    5. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    6. Braun, Julia & Sabanés Bové, Daniel & Held, Leonhard, 2014. "Choice of generalized linear mixed models using predictive crossvalidation," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 190-202.
    7. Fantazzini, Dean & Shakleina, Marina & Yuras, Natalia, 2018. "Big Data for computing social well-being indices of the Russian population," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 43-66.
    8. 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.
    9. David Kaplan, 2021. "On the Quantification of Model Uncertainty: A Bayesian Perspective," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 215-238, March.
    10. Mingan Yang & Min Wang & Guanghui Dong, 2020. "Bayesian variable selection for mixed effects model with shrinkage prior," Computational Statistics, Springer, vol. 35(1), pages 227-243, March.
    11. Heyard, Rachel & Held, Leonhard, 2019. "The quantile probability model," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 84-99.
    12. Jeffrey S. Racine & Qi Li & Dalei Yu & Li Zheng, 2023. "Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1251-1261, October.
    13. Armagan, Artin & Dunson, David, 2011. "Sparse variational analysis of linear mixed models for large data sets," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1056-1062, August.
    14. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    15. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    16. Righi, Simone & Pancotto, Francesca & Giardini, Francesca, 2022. "Cooperation, fairness and civic capital after an earthquake: Evidence from two Italian regions," SocArXiv n49hv_v1, Center for Open Science.
    17. 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.
    18. Hasan, Iftekhar & Horvath, Roman & Mares, Jan, 2020. "Finance and wealth inequality," Journal of International Money and Finance, Elsevier, vol. 108(C).
    19. Galharret, Jean-Michel & Philippe, Anne, 2023. "Bayesian analysis for mediation and moderation using g−priors," Econometrics and Statistics, Elsevier, vol. 27(C), pages 161-172.
    20. Philipp Piribauer & Jesús Crespo Cuaresma, 2016. "Bayesian Variable Selection in Spatial Autoregressive Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(4), pages 457-479, October.

    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:bpj:ijbist:v:6:y:2010:i:1:n:33. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.