IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v115y2013icp33-47.html
   My bibliography  Save this article

The cluster bootstrap consistency in generalized estimating equations

Author

Listed:
  • Cheng, Guang
  • Yu, Zhuqing
  • Huang, Jianhua Z.

Abstract

The cluster bootstrap resamples clusters or subjects instead of individual observations in order to preserve the dependence within each cluster or subject. In this paper, we provide a theoretical justification of using the cluster bootstrap for the inferences of the generalized estimating equations (GEE) for clustered/longitudinal data. Under the general exchangeable bootstrap weights, we show that the cluster bootstrap yields a consistent approximation of the distribution of the regression estimate, and a consistent approximation of the confidence sets. We also show that a computationally more efficient one-step version of the cluster bootstrap provides asymptotically equivalent inference.

Suggested Citation

  • Cheng, Guang & Yu, Zhuqing & Huang, Jianhua Z., 2013. "The cluster bootstrap consistency in generalized estimating equations," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 33-47.
    • Handle: RePEc:eee:jmvana:v:115:y:2013:i:c:p:33-47
    DOI: 10.1016/j.jmva.2012.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X12002175
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2012.09.003?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. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    2. Lloyd A. Mancl & Timothy A. DeRouen, 2001. "A Covariance Estimator for GEE with Improved Small‐Sample Properties," Biometrics, The International Biometric Society, vol. 57(1), pages 126-134, March.
    3. Kauermann G. & Carroll R.J., 2001. "A Note on the Efficiency of Sandwich Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1387-1396, December.
    4. You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
    5. C. A. Field & A. H. Welsh, 2007. "Bootstrapping clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 369-390, June.
    6. You-Gan Wang & Vincent J. Carey, 2004. "Unbiased Estimating Equations From Working Correlation Models for Irregularly Timed Repeated Measures," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 845-853, January.
    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. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    2. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    3. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    4. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly & Kaspar Wüthrich, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 123-137, January.
    5. Anthony M. Evans & Joachim I. Krueger, 2014. "Outcomes and expectations in dilemmas of trust," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(2), pages 90-103, March.
    6. Martin Spiess & Pascal Jordan & Mike Wendt, 2019. "Simplified Estimation and Testing in Unbalanced Repeated Measures Designs," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 212-235, March.
    7. repec:cup:judgdm:v:9:y:2014:i:2:p:90-103 is not listed on IDEAS
    8. He, Hongwei & Hu, Yansong, 2021. "The dynamic impacts of shared leadership and the transactive memory system on team performance: A longitudinal study," Journal of Business Research, Elsevier, vol. 130(C), pages 14-26.
    9. Hailemichael M. Worku & Mark Rooij, 2018. "A Multivariate Logistic Distance Model for the Analysis of Multiple Binary Responses," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 124-146, April.
    10. Guang Cheng, 2013. "How Many Iterations are Sufficient for Efficient Semiparametric Estimation?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 592-618, September.
    11. Hudecová, Šárka & Pešta, Michal, 2013. "Modeling dependencies in claims reserving with GEE," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 786-794.
    12. Mark Rooij, 2018. "Transitional modeling of experimental longitudinal data with missing values," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(1), pages 107-130, 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. You-Gan Wang & Yuning Zhao, 2007. "A Modified Pseudolikelihood Approach for Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 63(3), pages 681-689, September.
    2. Di Shu & Jessica G. Young & Sengwee Toh & Rui Wang, 2021. "Variance estimation in inverse probability weighted Cox models," Biometrics, The International Biometric Society, vol. 77(3), pages 1101-1117, September.
    3. Zhang, Qiang & Ip, Edward H. & Pan, Junhao & Plemmons, Robert, 2017. "Individual-specific, sparse inverse covariance estimation in generalized estimating equations," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 96-103.
    4. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    5. Coisnon, Thomas & Rousselière, Damien & Rousselière, Samira, 2018. "Information on biodiversity and environmental behaviors: a European study of individual and institutional drivers to adopt sustainable gardening practices," Working Papers 272611, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    6. Carrillo, Paul E. & Onofa, Mercedes & Ponce, Juan, 2010. "Information Technology and Student Achievement: Evidence from a Randomized Experiment in Ecuador," IDB Publications (Working Papers) 3094, Inter-American Development Bank.
    7. Weidner, Martin & Zylkin, Thomas, 2021. "Bias and consistency in three-way gravity models," Journal of International Economics, Elsevier, vol. 132(C).
    8. Paniagua, Victoria, 2022. "When clients vote for brokers: How elections improve public goods provision in urban slums," World Development, Elsevier, vol. 158(C).
    9. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    10. Fu, Liya & Wang, You-Gan, 2016. "Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 492-502.
    11. Fan, Chunpeng & Zhang, Donghui, 2014. "Wald-type rank tests: A GEE approach," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 1-16.
    12. Liya Fu & Yangyang Hao & You-Gan Wang, 2018. "Working correlation structure selection in generalized estimating equations," Computational Statistics, Springer, vol. 33(2), pages 983-996, June.
    13. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    14. 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.
    15. 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.
    16. Slawa Rokicki & Jessica Cohen & Gunther Fink & Joshua Salomon & Mary Beth Landrum, 2018. "Inference with difference-in-differences with a small number of groups: a review, simulation study and empirical application using SHARE data," CHaRMS Working Papers 18-01, Centre for HeAlth Research at the Management School (CHaRMS).
    17. Xu, Jianwen & Wang, You-Gan, 2014. "Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 70-77.
    18. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
    19. Samuel D. Oman & Victoria Landsman & Yohay Carmel & Ronen Kadmon, 2007. "Analyzing Spatially Distributed Binary Data Using Independent-Block Estimating Equations," Biometrics, The International Biometric Society, vol. 63(3), pages 892-900, September.
    20. Blackburn, McKinley L., 2022. "Testing for coefficient differences across nested linear regression specifications," Econometrics and Statistics, Elsevier, vol. 23(C), pages 1-18.

    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:jmvana:v:115:y:2013:i:c:p:33-47. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.