IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v26y2011i1p121-144.html
   My bibliography  Save this article

Bayesian approach to analysing longitudinal bivariate binary data with informative dropout

Author

Listed:
  • Jennifer Chan
  • Wai Wan

Abstract

No abstract is available for this item.

Suggested Citation

  • Jennifer Chan & Wai Wan, 2011. "Bayesian approach to analysing longitudinal bivariate binary data with informative dropout," Computational Statistics, Springer, vol. 26(1), pages 121-144, March.
    • Handle: RePEc:spr:compst:v:26:y:2011:i:1:p:121-144
    DOI: 10.1007/s00180-010-0213-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-010-0213-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-010-0213-5?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. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    2. Chan, Jennifer S.K. & Kuk, Anthony Y.C. & Yam, Carrie H.K., 2005. "Monte Carlo approximation through Gibbs output in generalized linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 300-312, June.
    3. Montmarquette, Claude & Mahseredjian, Sophie & Houle, Rachel, 2001. "The determinants of university dropouts: a bivariate probability model with sample selection," Economics of Education Review, Elsevier, vol. 20(5), pages 475-484, October.
    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. David M. Murray & Jonathan L. Blitstein, 2003. "Methods To Reduce The Impact Of Intraclass Correlation In Group-Randomized Trials," Evaluation Review, , vol. 27(1), pages 79-103, February.
    2. Patrick E. B. FitzGerald, 2002. "Extended Generalized Estimating Equations for Binary Familial Data with Incomplete Families," Biometrics, The International Biometric Society, vol. 58(4), pages 718-726, December.
    3. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    4. Sinha, Sanjoy K. & Kaushal, Amit & Xiao, Wenzhong, 2014. "Inference for longitudinal data with nonignorable nonmonotone missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 77-91.
    5. E. Michael Foster & Grace Y. Fang, 2004. "Alternative Methods for Handling Attrition," Evaluation Review, , vol. 28(5), pages 434-464, October.
    6. Maria De Paola & Francesca Gioia, 2011. "Risk Aversion And Major Choice: Evidence From Italian Students," Working Papers 201107, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    7. Raquel Gilar-Corbi & Teresa Pozo-Rico & Juan-Luis Castejón & Tarquino Sánchez & Ivan Sandoval-Palis & Jack Vidal, 2020. "Academic Achievement and Failure in University Studies: Motivational and Emotional Factors," Sustainability, MDPI, vol. 12(23), pages 1-14, November.
    8. Walker, Ian & Zhu, Yu, 2011. "Differences by degree: Evidence of the net financial rates of return to undergraduate study for England and Wales," Economics of Education Review, Elsevier, vol. 30(6), pages 1177-1186.
    9. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The Economics of University Dropouts and Delayed Graduation: A Survey," IZA Discussion Papers 11421, Institute of Labor Economics (IZA).
    10. Mette Ejrnæs & Anders Holm, 2006. "Comparing Fixed Effects and Covariance Structure Estimators for Panel Data," Sociological Methods & Research, , vol. 35(1), pages 61-83, August.
    11. Geert Verbeke & Geert Molenberghs & Herbert Thijs & Emmanuel Lesaffre & Michael G. Kenward, 2001. "Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach," Biometrics, The International Biometric Society, vol. 57(1), pages 7-14, March.
    12. Rebecca E. Anthony & Amy L. Paine & Katherine H. Shelton, 2019. "Depression and Anxiety Symptoms of British Adoptive Parents: A Prospective Four-Wave Longitudinal Study," IJERPH, MDPI, vol. 16(24), pages 1-14, December.
    13. Stephen E. Childs & Ross Finnie & Felice Martinello, 2017. "Postsecondary Student Persistence and Pathways: Evidence From the YITS-A in Canada," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(3), pages 270-294, May.
    14. Miran A. Jaffa & Ayad A. Jaffa, 2019. "A Likelihood-Based Approach with Shared Latent Random Parameters for the Longitudinal Binary and Informative Censoring Processes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 597-613, December.
    15. Molenberghs, Geert & Verbeke, Geert & Thijs, Herbert & Lesaffre, Emmanuel & Kenward, Michael G., 2001. "Influence analysis to assess sensitivity of the dropout process," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 93-113, July.
    16. Shu Xu & Shelley A. Blozis, 2011. "Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 237-256, April.
    17. Sebastian Domhof & Edgar Brunner & D. Wayne Osgood, 2002. "Rank Procedures for Repeated Measures with Missing Values," Sociological Methods & Research, , vol. 30(3), pages 367-393, February.
    18. Chan, Jennifer S.K. & Leung, Doris Y.P. & Boris Choy, S.T. & Wan, Wai Y., 2009. "Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4530-4545, October.
    19. Bian, Yuan & Yi, Grace Y. & He, Wenqing, 2024. "A unified framework of analyzing missing data and variable selection using regularized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    20. Stratton, Leslie S. & O'Toole, Dennis M. & Wetzel, James N., 2006. "Are the Factors Affecting Dropout Behavior Related to Initial Enrollment Intensity for College Undergraduates?," IZA Discussion Papers 1951, Institute of Labor Economics (IZA).

    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:26:y:2011:i:1:p:121-144. 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.