IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v56y2012i5p1256-1274.html
   My bibliography  Save this article

Bayesian model selection for logistic regression models with random intercept

Author

Listed:
  • Wagner, Helga
  • Duller, Christine

Abstract

Data, collected to model risk of an interesting event, often have a multilevel structure as patients are clustered within larger units, e.g. clinical centers. Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters. Model specification requires to decide which regressors have a non-negligible effect, and hence, should be included in the final model and whether risk is actually heterogeneous among centers, i.e. whether the model should include a random intercept or not. In a Bayesian approach, these questions can be answered by combining variable selection with variance selection of the random intercept. Bayesian model selection is performed for a reparameterized version of the logistic random intercept model using spike and slab priors on the parameters subject to selection. Different specifications for these priors are compared on simulated data as well as on a data set where the goal is to identify risk factors for complications after endoscopic retrograde cholangiopancreatography (ERCP).

Suggested Citation

  • Wagner, Helga & Duller, Christine, 2012. "Bayesian model selection for logistic regression models with random intercept," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1256-1274.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1256-1274
    DOI: 10.1016/j.csda.2011.06.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311002507
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2011.06.033?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. A. Gelman & Y. Goegebeur & F. Tuerlinckx & I. Van Mechelen, 2000. "Diagnostic checks for discrete data regression models using posterior predictive simulations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 247-268.
    2. Ishwaran H. & Rao J.S., 2003. "Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 438-455, January.
    3. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    4. Smith M. & Kohn R., 2002. "Parsimonious Covariance Matrix Estimation for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1141-1153, December.
    5. Zhen Chen & David B. Dunson, 2003. "Random Effects Selection in Linear Mixed Models," Biometrics, The International Biometric Society, vol. 59(4), pages 762-769, December.
    6. Wagner, Helga & Tüchler, Regina, 2010. "Bayesian estimation of random effects models for multivariate responses of mixed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1206-1218, May.
    7. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    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. Shashank Vaid & Michael Ahearne & Ryan Krause, 2021. "Operations‐Related Structural Flux: Firm Performance Effects of Executives’ Appointments and Exits," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2188-2210, July.
    2. Sylvia Fruhwirth-Schnatter & Peter Knaus, 2022. "Sparse Bayesian State-Space and Time-Varying Parameter Models," Papers 2207.12147, arXiv.org.
    3. Chase N. Joyner & Christopher S. McMahan & Joshua M. Tebbs & Christopher R. Bilder, 2020. "From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data," Biometrics, The International Biometric Society, vol. 76(3), pages 913-923, September.
    4. Li He & Yu-Bo Wang & William C. Bridges & Zhulin He & S. Megan Che, 2023. "Bayesian Framework for Causal Inference with Principal Stratification and Clusters," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 114-140, April.

    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. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
    2. 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.
    3. Wang, Hao, 2010. "Sparse seemingly unrelated regression modelling: Applications in finance and econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2866-2877, November.
    4. Dimitris Korobilis, 2008. "Forecasting in vector autoregressions with many predictors," Advances in Econometrics, in: Bayesian Econometrics, pages 403-431, Emerald Group Publishing Limited.
    5. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    6. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, Universität Innsbruck.
    7. 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.
    8. Lee, Kyeong Eun & Kim, Yongku & Xu, Ronghui, 2014. "Bayesian variable selection under the proportional hazards mixed-effects model," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 53-65.
    9. Ling Zhou & Huazhen Lin & Xinyuan Song & Yi Li, 2014. "Selection of Latent Variables for Multiple Mixed-outcome Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1064-1082, December.
    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. Robert Kohn & Rachida Ouysse, 2007. "Bayesian Variable Selection of Risk Factors in the APT Model," Discussion Papers 2007-32, School of Economics, The University of New South Wales.
    12. 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.
    13. Yang, Mingan, 2012. "Bayesian variable selection for logistic mixed model with nonparametric random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2663-2674.
    14. Howard D. Bondell & Arun Krishna & Sujit K. Ghosh, 2010. "Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models," Biometrics, The International Biometric Society, vol. 66(4), pages 1069-1077, December.
    15. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian identification, selection and estimation of semiparametric functions in high-dimensional additive models," Journal of Econometrics, Elsevier, vol. 143(2), pages 291-316, April.
    16. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    17. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    18. Lam, Clifford, 2008. "Estimation of large precision matrices through block penalization," LSE Research Online Documents on Economics 31543, London School of Economics and Political Science, LSE Library.
    19. Benjamin R. Saville & Amy H. Herring, 2009. "Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors," Biometrics, The International Biometric Society, vol. 65(2), pages 369-376, June.
    20. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.

    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:eee:csdana:v:56:y:2012:i:5:p:1256-1274. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.