IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v42y2015i4p1214-1224.html
   My bibliography  Save this article

Model Selection Criterion Based on the Multivariate Quasi-Likelihood for Generalized Estimating Equations

Author

Listed:
  • Shinpei Imori

Abstract

type="main" xml:id="sjos12160-abs-0001"> The generalized estimating equations (GEE) approach has attracted considerable interest for the analysis of correlated response data. This paper considers the model selection criterion based on the multivariate quasi-likelihood (MQL) in the GEE framework. The GEE approach is closely related to the MQL. We derive a necessary and sufficient condition for the uniqueness of the risk function based on the MQL by using properties of differential geometry. Furthermore, we establish a formal derivation of model selection criterion as an asymptotically unbiased estimator of the prediction risk under this condition, and we explicitly take into account the effect of estimating the correlation matrix used in the GEE procedure.

Suggested Citation

  • Shinpei Imori, 2015. "Model Selection Criterion Based on the Multivariate Quasi-Likelihood for Generalized Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1214-1224, December.
  • Handle: RePEc:bla:scjsta:v:42:y:2015:i:4:p:1214-1224
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/sjos.12160
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Wei Pan, 2002. "Goodness‐of‐fit Tests for GEE with Correlated Binary Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 101-110, March.
    2. Eva Cantoni & Joanna Mills Flemming & Elvezio Ronchetti, 2005. "Variable Selection for Marginal Longitudinal Generalized Linear Models," Biometrics, The International Biometric Society, vol. 61(2), pages 507-514, June.
    3. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    4. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.
    5. 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.
    6. Wei Pan, 2001. "Model Selection in Estimating Equations," Biometrics, The International Biometric Society, vol. 57(2), pages 529-534, June.
    Full references (including those not matched with items on IDEAS)

    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. Lan Wang & Annie Qu, 2009. "Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 177-190, January.
    2. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.
    3. Chung‐Wei Shen & Yi‐Hau Chen, 2018. "Model selection for semiparametric marginal mean regression accounting for within‐cluster subsampling variability and informative cluster size," Biometrics, The International Biometric Society, vol. 74(3), pages 934-943, September.
    4. Lin, Hui-Yi & Myers, Leann, 2006. "Power and Type I error rates of goodness-of-fit statistics for binomial generalized estimating equations (GEE) models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3432-3448, August.
    5. Li, Gaorong & Lian, Heng & Feng, Sanying & Zhu, Lixing, 2013. "Automatic variable selection for longitudinal generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 174-186.
    6. Blommaert, A. & Hens, N. & Beutels, Ph., 2014. "Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 667-680.
    7. Geronimi, J. & Saporta, G., 2017. "Variable selection for multiply-imputed data with penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 103-114.
    8. Marc Aerts & Niel Hens & Jeffrey Simonoff, 2010. "Model selection in regression based on pre-smoothing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1455-1472.
    9. Jakub Stoklosa & Heloise Gibb & David I. Warton, 2014. "Fast forward selection for generalized estimating equations with a large number of predictor variables," Biometrics, The International Biometric Society, vol. 70(1), pages 110-120, March.
    10. Chung‐Wei Shen & Chun‐Shu Chen, 2024. "Estimation and selection for spatial zero‐inflated count models," Environmetrics, John Wiley & Sons, Ltd., vol. 35(4), June.
    11. Kwon, Yongchan & Choi, Young-Geun & Park, Taesung & Ziegler, Andreas & Paik, Myunghee Cho, 2017. "Generalized estimating equations with stabilized working correlation structure," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 1-11.
    12. Fan, Yali & Qin, Guoyou & Zhu, Zhongyi, 2012. "Variable selection in robust regression models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 156-167.
    13. Lan Wang & Jianhui Zhou & Annie Qu, 2012. "Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 68(2), pages 353-360, June.
    14. Wei Pan, 2001. "Model Selection in Estimating Equations," Biometrics, The International Biometric Society, vol. 57(2), pages 529-534, June.
    15. 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.
    16. Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Zafar Nazarov, 2011. "Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys," Working Papers WR-887-1, RAND Corporation.
    17. Katrina N. Burns & Kan Sun & Julius N. Fobil & Richard L. Neitzel, 2016. "Heart Rate, Stress, and Occupational Noise Exposure among Electronic Waste Recycling Workers," IJERPH, MDPI, vol. 13(1), pages 1-16, January.
    18. Song Guo & Feng Ling & Juan Hou & Jinna Wang & Guiming Fu & Zhenyu Gong, 2014. "Mosquito Surveillance Revealed Lagged Effects of Mosquito Abundance on Mosquito-Borne Disease Transmission: A Retrospective Study in Zhejiang, China," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-8, November.
    19. Marc-Andreas Muendler & Sascha O. Becker, 2010. "Margins of Multinational Labor Substitution," American Economic Review, American Economic Association, vol. 100(5), pages 1999-2030, December.
    20. Laura Neumeyer & Anna Gründler & Anna-Luisa Stöber, 2023. "Don’t Worry, Be Happy—Does the CEO’s Personality Mitigate the Negative Effect of Financial Constraints on Employee Satisfaction?," Schmalenbach Journal of Business Research, Springer, vol. 75(1), pages 71-98, March.

    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:bla:scjsta:v:42:y:2015:i:4:p:1214-1224. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.