IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v48y2005i4p857-868.html
   My bibliography  Save this article

Bayesian computation for logistic regression

Author

Listed:
  • Groenewald, Pieter C. N.
  • Mokgatlhe, Lucky

Abstract

No abstract is available for this item.

Suggested Citation

  • Groenewald, Pieter C. N. & Mokgatlhe, Lucky, 2005. "Bayesian computation for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 857-868, April.
  • Handle: RePEc:eee:csdana:v:48:y:2005:i:4:p:857-868
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00114-8
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
    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. Silvia Figini & Paolo Giudici, 2013. "Credit risk predictions with Bayesian model averaging," DEM Working Papers Series 034, University of Pavia, Department of Economics and Management.
    2. Katrin Dippold & Harald Hruschka, 2013. "Variable selection for market basket analysis," Computational Statistics, Springer, vol. 28(2), pages 519-539, April.
    3. Dippold, Katrin & Hruschka, Harald, 2010. "Variable Selection for Market Basket Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 443, University of Regensburg, Department of Economics.
    4. Schyan Zafar & Geoff K. Nicholls, 2022. "Measuring diachronic sense change: New models and Monte Carlo methods for Bayesian inference," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1569-1604, November.

    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. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    2. Min, Chung-ki, 1998. "A Gibbs sampling approach to estimation and prediction of time-varying-parameter models," Computational Statistics & Data Analysis, Elsevier, vol. 27(2), pages 171-194, April.
    3. Edsel A. Peña & Wensong Wu & Walter Piegorsch & Ronald W. West & LingLing An, 2017. "Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 716-732, April.
    4. Posch Peter N. & Loeffler Gunter & Schoene Christiane, 2005. "Bayesian Methods for Improving Credit Scoring Models," Finance 0505024, University Library of Munich, Germany.
    5. Poirier, Dale J., 1996. "A Bayesian analysis of nested logit models," Journal of Econometrics, Elsevier, vol. 75(1), pages 163-181, November.
    6. Dorfman, Jeffrey H., 1995. "A numerical bayesian test for cointegration of AR processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 289-324.
    7. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2008. "Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4608-4624, June.
    8. Peter E. Rossi, 1984. "Convergence of Integrals Encountered in Dichotomous Dependent Variable Problems," Discussion Papers 588, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    9. DeSarbo, Wayne S. & Kim, Youngchan & Fong, Duncan, 1998. "A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 79-108, November.
    10. Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.
    11. William E. Griffiths & R. Carter Hill & Christopher J. O'Donnell, 2001. "Including Prior Information in Probit Model Estimation," Department of Economics - Working Papers Series 816, The University of Melbourne.
    12. Peter Haan & Daniel Kemptner & Arne Uhlendorff, 2015. "Bayesian procedures as a numerical tool for the estimation of an intertemporal discrete choice model," Empirical Economics, Springer, vol. 49(3), pages 1123-1141, November.
    13. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
    14. John Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
    15. Johan Koskinen & Sten-Ã…ke Stenberg, 2012. "Bayesian Analysis of Multilevel Probit Models for Data With Friendship Dependencies," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 203-230, April.
    16. Poirier, Dale J., 2012. "Perfect classifiers in partial observability bivariate probit," Economics Letters, Elsevier, vol. 116(3), pages 361-362.
    17. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Roger D. Weiss, 2020. "Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 890-904, December.
    18. Gabriele B. Durrant & Chris Skinner, 2006. "Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly pay," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 605-623, July.
    19. Marsh, L.C.Lawrence C., 2004. "The econometrics of higher education: editor's view," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 1-18.
    20. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

    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:eee:csdana:v:48:y:2005:i:4:p:857-868. 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.