IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v33y2018i2d10.1007_s00180-017-0755-x.html
   My bibliography  Save this article

Logistic regression diagnostics in ridge regression

Author

Listed:
  • M. Revan Özkale

    (Çukurova University)

  • Stanley Lemeshow

    (The Ohio State University)

  • Rodney Sturdivant

    (Azusa Pacific University)

Abstract

The adverse effects of multicollinearity and unusual observations are seen in logistic regression and attention had been given in the literature to each of these problems separately. However, multicollinearity and unusual observations can arise simultaneously in logistic regression. The objective of this paper is to propose the statistics for detecting the unusual observations in an ill-conditioned data set under the ridge logistic estimator. A numerical example and two Monte Carlo simulation studies are used to illustrate the methodology. The present investigation shows that ridge logistic estimation copes with unusual observations by downweighting their influence.

Suggested Citation

  • M. Revan Özkale & Stanley Lemeshow & Rodney Sturdivant, 2018. "Logistic regression diagnostics in ridge regression," Computational Statistics, Springer, vol. 33(2), pages 563-593, June.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0755-x
    DOI: 10.1007/s00180-017-0755-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-017-0755-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-017-0755-x?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. Jahufer, Aboobacker & Jianbao, Chen, 2009. "Assessing global influential observations in modified ridge regression," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 513-518, February.
    2. D. A. Williams, 1987. "Generalized Linear Model Diagnostics Using the Deviance and Single Case Deletions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 181-191, June.
    3. Preisser, John S. & Garcia, Daniel I., 2005. "Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 755-764, April.
    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. M. Revan Özkale & Engin Arıcan, 2019. "A first-order approximated jackknifed ridge estimator in binary logistic regression," Computational Statistics, Springer, vol. 34(2), pages 683-712, June.

    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. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
    2. Preisser, John S. & Garcia, Daniel I., 2005. "Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 755-764, April.
    3. Hadi Emami, 2018. "Local influence for Liu estimators in semiparametric linear models," Statistical Papers, Springer, vol. 59(2), pages 529-544, June.
    4. Rubén Moreno-Opo & Mariana Fernández-Olalla & Antoni Margalida & Ángel Arredondo & Francisco Guil, 2012. "Effect of Methodological and Ecological Approaches on Heterogeneity of Nest-Site Selection of a Long-Lived Vulture," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    5. Parra Álvarez, Juan Carlos & Misas A., Martha & López-Enciso, Enrique Antonio, 2011. "Heterogeneidad en la fijación de precios en Colombia : análisis de sus determinantes a partir de modelos de conteo," Chapters, in: López Enciso, Enrique & Ramírez Giraldo, María Teresa (ed.), Formación de precios y salarios en Colombia T.1, volume 1, chapter 8, pages 251-293, Banco de la Republica de Colombia.
    6. Monfort, Abel & Villagra, Nuria & Sánchez, Joaquín, 2021. "Economic impact of corporate foundations: An event analysis approach," Journal of Business Research, Elsevier, vol. 122(C), pages 159-170.
    7. T. Söküt Açar & M.R. Özkale, 2016. "Influence measures based on confidence ellipsoids in general linear regression model with correlated regressors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2791-2812, November.
    8. Li, Zaixing & Xu, Wangli & Zhu, Lixing, 2009. "Influence diagnostics and outlier tests for varying coefficient mixed models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2002-2017, October.
    9. Boehm, Martin, 2008. "Determining the impact of internet channel use on a customer's lifetime," Journal of Interactive Marketing, Elsevier, vol. 22(3), pages 2-22.
    10. Cordeiro, Gauss M. & Simas, Alexandre B., 2009. "The distribution of Pearson residuals in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3397-3411, July.
    11. José Osvaldo De Sordi & Marco Antonio Conejero & Manuel Meireles, 2016. "Bibliometric indicators in the context of regional repositories: proposing the D-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 235-258, April.
    12. Hammill, Bradley G. & Preisser, John S., 2006. "A SAS/IML software program for GEE and regression diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1197-1212, November.
    13. Shiyu Wang & Houping Xiao & Allan Cohen, 2021. "Adaptive Weight Estimation of Latent Ability: Application to Computerized Adaptive Testing With Response Revision," Journal of Educational and Behavioral Statistics, , vol. 46(5), pages 560-591, October.
    14. Saemi Choi & Jae Gon Lee & A-reum Lee & Chang Soo Eun & Dong Soo Han & Chan Hyuk Park, 2019. "Helicobacter pylori antibody and pepsinogen testing for predicting gastric microbiome abundance," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-14, December.
    15. Hadi Emami & Mostafa Emami, 2016. "New influence diagnostics in ridge regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 476-489, March.
    16. Juliana Scudilio & Gustavo H. A. Pereira, 2020. "Adjusted quantile residual for generalized linear models," Computational Statistics, Springer, vol. 35(1), pages 399-421, March.
    17. Aboobacker Jahufer & Jianbao Chen, 2012. "Identifying local influential observations in Liu estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(3), pages 425-438, April.
    18. Johan Koskinen & Peng Wang & Garry Robins & Philippa Pattison, 2018. "Outliers and Influential Observations in Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 809-830, December.
    19. Xie, Xian-Jin & Pendergast, Jane & Clarke, William, 2008. "Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2703-2713, January.
    20. Munoz-Garcia, J. & Munoz-Pichardo, J.M. & Pardo, L., 2006. "Cressie and Read power-divergences as influence measures for logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3199-3221, July.

    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:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0755-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.