IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v67y2013i3p293-310.html
   My bibliography  Save this article

Analysis of binary data with the possibility of wrong ascertainment

Author

Listed:
  • Surupa Roy
  • Kalyan Das
  • Angshuman Sarkar

Abstract

No abstract is available for this item.

Suggested Citation

  • Surupa Roy & Kalyan Das & Angshuman Sarkar, 2013. "Analysis of binary data with the possibility of wrong ascertainment," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 293-310, August.
    • Handle: RePEc:bla:stanee:v:67:y:2013:i:3:p:293-310
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/stan.12008
    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. Ramalho, Esmeralda A., 2007. "Binary models with misclassification in the variable of interest and nonignorable nonresponse," Economics Letters, Elsevier, vol. 96(1), pages 70-76, July.
    2. John M. Neuhaus, 2002. "Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification," Biometrics, The International Biometric Society, vol. 58(3), pages 675-683, September.
    3. Wai-Yin Poon & Hai-Bin Wang, 2010. "Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 498-520, September.
    4. Paulino, Carlos Daniel & Silva, Giovani & Alberto Achcar, Jorge, 2005. "Bayesian analysis of correlated misclassified binary data," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1120-1131, 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. Lizbeth Naranjo & Luz Judith R. Esparza & Carlos J. Pérez, 2020. "A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process," Mathematics, MDPI, vol. 8(4), pages 1-12, April.
    2. Frénay, Benoît & Doquire, Gauthier & Verleysen, Michel, 2014. "Estimating mutual information for feature selection in the presence of label noise," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 832-848.
    3. 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.
    4. Shun Yu & Xianzheng Huang, 2019. "Link misspecification in generalized linear mixed models with a random intercept for binary responses," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 827-843, September.
    5. Tang, Man-Lai & Qiu, Shi-Fang & Poon, Wai-Yin, 2012. "Confidence interval construction for disease prevalence based on partial validation series," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1200-1220.
    6. Brajendra C. Sutradhar, 2022. "Fixed versus Mixed Effects Based Marginal Models for Clustered Correlated Binary Data: an Overview on Advances and Challenges," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 259-302, May.
    7. Sajad Shojaee & Nastaran Hajizadeh & Hadis Najafimehr & Luca Busani & Mohamad Amin Pourhoseingholi & Ahmad Reza Baghestani & Maryam Nasserinejad & Sara Ashtari & Mohammad Reza Zali, 2018. "Bayesian adjustment for trend of colorectal cancer incidence in misclassified registering across Iranian provinces," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-10, December.
    8. Liu, Feng & Zhao, Shaoqiong & Li, Yang, 2017. "How many, how often, and how new? A multivariate profiling of mobile app users," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 71-80.
    9. Abidemi K. Adeniji & Steven H. Belle & Abdus S. Wahed, 2014. "Incorporating diagnostic accuracy into the estimation of discrete survival function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 60-72, January.
    10. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(1), pages 343-364.
    11. Rana, Subrata & Roy, Surupa & Das, Kalyan, 2018. "Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: An application to Alzheimer’s disease study," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 62-77.
    12. Yanlin Tang & Xinyuan Song & Grace Yun Yi, 2022. "Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 139-168, January.
    13. Robert H. Lyles & John M. Williamson & Hung-Mo Lin & Charles M. Heilig, 2005. "Extending McNemar's Test: Estimation and Inference When Paired Binary Outcome Data Are Misclassified," Biometrics, The International Biometric Society, vol. 61(1), pages 287-294, March.
    14. Wenqi Wu & James Stamey & David Kahle, 2015. "A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data," IJERPH, MDPI, vol. 12(9), pages 1-14, August.
    15. James D. Stamey & Daniel P. Beavers & Michael E. Sherr, 2017. "Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification," Sociological Methods & Research, , vol. 46(4), pages 772-792, November.
    16. Helmut Küchenhoff & Samuel M. Mwalili & Emmanuel Lesaffre, 2006. "A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX," Biometrics, The International Biometric Society, vol. 62(1), pages 85-96, March.
    17. Qiu, Shi-Fang & Poon, Wai-Yin & Tang, Man-Lai, 2016. "Confidence intervals for an ordinal effect size measure based on partially validated series," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 170-192.
    18. Tong-Yu Lu & Wai-Yin Poon & Siu Cheung, 2014. "A Unified Framework for the Comparison of Treatments with Ordinal Responses," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 605-620, October.
    19. Debjit Sengupta & Tathagata Banerjee & Surupa Roy, 2020. "Estimation of Poisson mean with under‐reported counts: a double sampling approach," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 508-535, December.

    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:stanee:v:67:y:2013:i:3:p:293-310. 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=0039-0402 .

    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.