IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v37y2010i8p1355-1368.html
   My bibliography  Save this article

Bayesian outlier analysis in binary regression

Author

Listed:
  • Aparecida Souza
  • Helio Migon

Abstract

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Suggested Citation

  • Aparecida Souza & Helio Migon, 2010. "Bayesian outlier analysis in binary regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1355-1368.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1355-1368
    DOI: 10.1080/02664760903031153
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903031153
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760903031153?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. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    2. Thaís C. O. Fonseca & Marco A. R. Ferreira & Helio S. Migon, 2008. "Objective Bayesian analysis for the Student-t regression model," Biometrika, Biometrika Trust, vol. 95(2), pages 325-333.
    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. Cristiano C. Santos & Rosangela H. Loschi, 2017. "Maximum likelihood estimation and parameter interpretation in elliptical mixed logistic regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 209-230, March.

    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. Aldo M. Garay & Heleno Bolfarine & Victor H. Lachos & Celso R.B. Cabral, 2015. "Bayesian analysis of censored linear regression models with scale mixtures of normal distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2694-2714, December.
    2. Zellner, Arnold & Ando, Tomohiro, 2010. "Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting," International Journal of Forecasting, Elsevier, vol. 26(2), pages 413-434, April.
    3. Hedibert F. Lopes & Nicholas G. Polson, 2016. "Particle Learning for Fat-Tailed Distributions," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1666-1691, December.
    4. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    5. Olawale Awe O. & Adedayo Adepoju A., 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 258-293, June.
    6. Justin L. Tobias & Mingliang Li, 2003. "A finite-sample hierarchical analysis of wage variation across public high schools: evidence from the NLSY and high school and beyond," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(3), pages 315-336.
    7. Chen, Cathy W.S. & Gerlach, Richard H. & Tai, Amanda P.J., 2008. "Testing for nonlinearity in mean and volatility for heteroskedastic models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 489-499.
    8. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    9. Panagiotelis, Anastasios & Smith, Michael, 2010. "Bayesian skew selection for multivariate models," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1824-1839, July.
    10. Junrong Liu & Robin C. Sickles & E. G. Tsionas, 2017. "Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity," Econometrics, MDPI, vol. 5(3), pages 1-21, July.
    11. Markku Lanne & Arto Luoma & Jani Luoto, 2012. "Bayesian Model Selection And Forecasting In Noncausal Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 812-830, August.
    12. Miranowski, John A. & Monchuk, Daniel C., 2004. "Spatial Labor Markets and Technology Spillovers - Analysis from the US Midwest," Working Papers 18224, Iowa State University, Department of Economics.
    13. Haroon Mumtaz & Fulvia Marotta, 2023. "Vulnerability to Climate Change: Evidence from a Dynamic Factor Model," Working Papers 961, Queen Mary University of London, School of Economics and Finance.
    14. Doris A. Oberdabernig & Stefan Humer & Jesus Crespo Cuaresma, 2018. "Democracy, Geography and Model Uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(2), pages 154-185, May.
    15. Moeltner, Klaus, 2019. "Bayesian nonlinear meta regression for benefit transfer," Journal of Environmental Economics and Management, Elsevier, vol. 93(C), pages 44-62.
    16. Antonio Parisi & B. Liseo, 2018. "Objective Bayesian analysis for the multivariate skew-t model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 277-295, June.
    17. Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Inference under Sampling from Scale Mixtures of Normals," Discussion Paper 1996-02, Tilburg University, Center for Economic Research.
    18. Susan L. Ettner & Betsy L. Cadwell & Louise B. Russell & Arleen Brown & Andrew J. Karter & Monika Safford & Carol Mangione & Gloria Beckles & William H. Herman & Theodore J. Thompson & and The TRIAD S, 2009. "Investing time in health: do socioeconomically disadvantaged patients spend more or less extra time on diabetes self‐care?," Health Economics, John Wiley & Sons, Ltd., vol. 18(6), pages 645-663, June.
    19. Bassetti, Federico & De Giuli, Maria Elena & Nicolino, Enrica & Tarantola, Claudia, 2018. "Multivariate dependence analysis via tree copula models: An application to one-year forward energy contracts," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1107-1121.
    20. Francesco Pattarin, 2018. "Spending Policies of Italian Banking Foundations," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0071, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".

    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:taf:japsta:v:37:y:2010:i:8:p:1355-1368. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.