IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v28y2013i4p1681-1698.html
   My bibliography  Save this article

Estimation of intra-cluster correlation coefficient via the failure of Bartlett’s second identity

Author

Listed:
  • Tsung-Shan Tsou
  • Wan-Chen Chen

Abstract

A new means of estimating the correlation coefficient for cluster binary data in the regression settings is introduced. The creation of this method is founded upon the violation of Bartlett’s second identity when adopting the binomial distributions to model binary data that are correlated. The new methodology applies to any sensible link functions that connect the success probability and covariates. One can easily implement the procedure by using any statistical software providing the naïve and the sandwich covariance matrices for regression parameter estimates. Simulations and real data analyses are used to demonstrate the efficacy of our new procedure. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Tsung-Shan Tsou & Wan-Chen Chen, 2013. "Estimation of intra-cluster correlation coefficient via the failure of Bartlett’s second identity," Computational Statistics, Springer, vol. 28(4), pages 1681-1698, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1681-1698
    DOI: 10.1007/s00180-012-0372-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-012-0372-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-012-0372-7?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. Terra L. Slaton & Walter W. Piegorsch & Stephen D. Durham, 2000. "Estimation and Testing with Overdispersed Proportions Using the Beta-Logistic Regression Model of Heckman and Willis," Biometrics, The International Biometric Society, vol. 56(1), pages 125-133, March.
    2. Martin S. Ridout & Clarice G. B. Demétrio & David Firth, 1999. "Estimating Intraclass Correlation for Binary Data," Biometrics, The International Biometric Society, vol. 55(1), pages 137-148, March.
    3. Presnell, Brett & Boos, Dennis D., 2004. "The IOS Test for Model Misspecification," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 216-227, January.
    4. Bahjat F. Qaqish, 2003. "A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations," Biometrika, Biometrika Trust, vol. 90(2), pages 455-463, June.
    5. Stefanski L. A. & Boos D. D., 2002. "The Calculus of M-Estimation," The American Statistician, American Statistical Association, vol. 56, pages 29-38, February.
    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. Guosheng Yin & Yu Shen, 2005. "Adaptive Design and Estimation in Randomized Clinical Trials with Correlated Observations," Biometrics, The International Biometric Society, vol. 61(2), pages 362-369, June.
    2. Adam C. Sales & Ben B. Hansen, 2020. "Limitless Regression Discontinuity," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 143-174, April.
    3. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    4. McMahon, James M. & Pouget, Enrique R. & Tortu, Stephanie, 2006. "A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3663-3680, August.
    5. Yue, Chen & Chen, Shaojie & Sair, Haris I. & Airan, Raag & Caffo, Brian S., 2015. "Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 126-133.
    6. Jorge A. Sefair & Oscar Guaje & Andrés L. Medaglia, 2021. "A column-oriented optimization approach for the generation of correlated random vectors," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 777-808, September.
    7. Y. Fong & J. Wakefield & S. De Rosa & N. Frahm, 2012. "A Robust Bayesian Random Effects Model for Nonlinear Calibration Problems," Biometrics, The International Biometric Society, vol. 68(4), pages 1103-1112, December.
    8. Luca Greco & Laura Ventura, 2011. "Robust inference for the stress–strength reliability," Statistical Papers, Springer, vol. 52(4), pages 773-788, November.
    9. Tounkara Fodé & Rivest Louis-Paul, 2014. "Some New Random Effect Models for Correlated Binary Responses," Dependence Modeling, De Gruyter, vol. 2(1), pages 1-15, December.
    10. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    11. Sergei Leonov & Bahjat Qaqish, 2020. "Correlated endpoints: simulation, modeling, and extreme correlations," Statistical Papers, Springer, vol. 61(2), pages 741-766, April.
    12. Moysiadis, Theodoros & Fokianos, Konstantinos, 2014. "On binary and categorical time series models with feedback," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 209-228.
    13. Donald P. Green & Winston Lin & Claudia Gerber, 2018. "Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments," Evaluation Review, , vol. 42(4), pages 391-422, August.
    14. Berman, Oded & Krass, Dmitry & Menezes, Mozart B.C., 2013. "Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns," Omega, Elsevier, vol. 41(4), pages 766-779.
    15. B. C. Sutradhar, 2008. "On auto-regression type dynamic mixed models for binary panel data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 209-221.
    16. Farrell, Patrick J. & Sutradhar, Brajendra C., 2006. "A non-linear conditional probability model for generating correlated binary data," Statistics & Probability Letters, Elsevier, vol. 76(4), pages 353-361, February.
    17. I Ciocănea-Teodorescu & E E Gabriel & A Sjölander, 2022. "Sensitivity analysis for unmeasured confounding in the estimation of marginal causal effects [Doubly robust estimation in missing data and causal inference models]," Biometrika, Biometrika Trust, vol. 109(4), pages 1101-1116.
    18. Lennart Bondesson & Daniel Thorburn, 2008. "A List Sequential Sampling Method Suitable for Real‐Time Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 466-483, September.
    19. Janie McDonald & Patrick D. Gerard & Christopher S. McMahan & William R. Schucany, 2016. "Exact-Permutation-Based Sign Tests for Clustered Binary Data Via Weighted and Unweighted Test Statistics," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 698-712, December.
    20. Matthew W. Guerra & Justine Shults, 2014. "A Note on the Simulation of Overdispersed Random Variables With Specified Marginal Means and Product Correlations," The American Statistician, Taylor & Francis Journals, vol. 68(2), pages 104-107, May.

    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:28:y:2013:i:4:p:1681-1698. 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.