IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v67y2011i2p629-635.html
   My bibliography  Save this article

Likelihood Methods for Binary Responses of Present Components in a Cluster

Author

Listed:
  • Xiaoyun Li
  • Dipankar Bandyopadhyay
  • Stuart Lipsitz
  • Debajyoti Sinha

Abstract

No abstract is available for this item.

Suggested Citation

  • Xiaoyun Li & Dipankar Bandyopadhyay & Stuart Lipsitz & Debajyoti Sinha, 2011. "Likelihood Methods for Binary Responses of Present Components in a Cluster," Biometrics, The International Biometric Society, vol. 67(2), pages 629-635, June.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:2:p:629-635
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01483.x
    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. John M. Williamson & Somnath Datta & Glen A. Satten, 2003. "Marginal Analyses of Clustered Data When Cluster Size Is Informative," Biometrics, The International Biometric Society, vol. 59(1), pages 36-42, March.
    2. David B. Dunson & Zhen Chen & Jean Harry, 2003. "A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 521-530, September.
    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. Ling Lan & Dipankar Bandyopadhyay & Somnath Datta, 2017. "Non-parametric regression in clustered multistate current status data with informative cluster size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 31-57, January.
    2. Shaun R. Seaman & Menelaos Pavlou & Andrew J. Copas, 2014. "Methods for observed-cluster inference when cluster size is informative: A review and clarifications," Biometrics, The International Biometric Society, vol. 70(2), pages 449-456, June.
    3. Iraj Kazemi & Fatemeh Hassanzadeh, 2021. "Marginalized random-effects models for clustered binomial data through innovative link functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 197-228, June.
    4. Laura Boehm & Brian J. Reich & Dipankar Bandyopadhyay, 2013. "Bridging Conditional and Marginal Inference for Spatially Referenced Binary Data," Biometrics, The International Biometric Society, vol. 69(2), pages 545-554, June.
    5. Bruce J. Swihart & Brian S. Caffo & Ciprian M. Crainiceanu, 2014. "A Unifying Framework for Marginalised Random-Intercept Models of Correlated Binary Outcomes," International Statistical Review, International Statistical Institute, vol. 82(2), pages 275-295, August.
    6. Dipankar Bandyopadhyay & Antonio Canale, 2016. "Non-parametric spatial models for clustered ordered periodontal data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 619-640, August.

    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. Jaakko Nevalainen & Somnath Datta & Hannu Oja, 2014. "Inference on the marginal distribution of clustered data with informative cluster size," Statistical Papers, Springer, vol. 55(1), pages 71-92, February.
    2. Chun Yin Lee & Kin Yau Wong & Kwok Fai Lam & Dipankar Bandyopadhyay, 2023. "A semiparametric joint model for cluster size and subunit‐specific interval‐censored outcomes," Biometrics, The International Biometric Society, vol. 79(3), pages 2010-2022, September.
    3. Faes, Christel & Geys, Helena & Aerts, Marc & Molenberghs, Geert, 2006. "A hierarchical modeling approach for risk assessment in developmental toxicity studies," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1848-1861, December.
    4. Glen McGee & Marianthi‐Anna Kioumourtzoglou & Marc G. Weisskopf & Sebastien Haneuse & Brent A. Coull, 2020. "On the interplay between exposure misclassification and informative cluster size," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1209-1226, November.
    5. Zhen Pang & Anthony Y. C. Kuk, 2007. "Test of Marginal Compatibility and Smoothing Methods for Exchangeable Binary Data with Unequal Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(1), pages 218-227, March.
    6. Shaun R. Seaman & Menelaos Pavlou & Andrew J. Copas, 2014. "Methods for observed-cluster inference when cluster size is informative: A review and clarifications," Biometrics, The International Biometric Society, vol. 70(2), pages 449-456, June.
    7. Zhang, Xinyan & Sun, Jianguo, 2010. "Regression analysis of clustered interval-censored failure time data with informative cluster size," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1817-1823, July.
    8. Joanna H. Shih & Michael P. Fay, 2017. "Pearson's chi-square test and rank correlation inferences for clustered data," Biometrics, The International Biometric Society, vol. 73(3), pages 822-834, September.
    9. Ling Lan & Dipankar Bandyopadhyay & Somnath Datta, 2017. "Non-parametric regression in clustered multistate current status data with informative cluster size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 31-57, January.
    10. Lanjia Lin & Dipankar Bandyopadhyay & Stuart R. Lipsitz & Debajyoti Sinha, 2010. "Association Models for Clustered Data with Binary and Continuous Responses," Biometrics, The International Biometric Society, vol. 66(1), pages 287-293, March.
    11. Ying Huang & Brian Leroux, 2011. "Informative Cluster Sizes for Subcluster-Level Covariates and Weighted Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 67(3), pages 843-851, September.
    12. Sally Hunsberger & Lori Long & Sarah E. Reese & Gloria H. Hong & Ian A. Myles & Christa S. Zerbe & Pleonchan Chetchotisakd & Joanna H. Shih, 2022. "Rank correlation inferences for clustered data with small sample size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(3), pages 309-330, August.
    13. Jaakko Nevalainen & Denis Larocque & Hannu Oja, 2007. "A weighted spatial median for clustered data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 355-379, February.
    14. Somnath Datta & Glen A. Satten, 2008. "A Signed-Rank Test for Clustered Data," Biometrics, The International Biometric Society, vol. 64(2), pages 501-507, June.
    15. Reem Aljarallah & Samer A Kharroubi, 2021. "Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation," Mathematics, MDPI, vol. 9(3), pages 1-11, January.
    16. Seo, Byungtae & Ha, Il Do, 2024. "Semiparametric accelerated failure time models under unspecified random effect distributions," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).
    17. You-Gan Wang & Yudong Zhao, 2008. "Weighted Rank Regression for Clustered Data Analysis," Biometrics, The International Biometric Society, vol. 64(1), pages 39-45, March.
    18. Michael R. Elliott & Marshall M. Joffe & Zhen Chen, 2006. "A Potential Outcomes Approach to Developmental Toxicity Analyses," Biometrics, The International Biometric Society, vol. 62(2), pages 352-360, June.
    19. Claudia Czado & Anette Heyn & Gernot Müller, 2011. "Modeling individual migraine severity with autoregressive ordered probit models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 101-121, March.
    20. Julie S. Najita & Yi Li & Paul J. Catalano, 2009. "A novel application of a bivariate regression model for binary and continuous outcomes to studies of fetal toxicity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 555-573, September.

    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:biomet:v:67:y:2011:i:2:p:629-635. 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=0006-341X .

    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.