IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v50y2006i12p3432-3448.html
   My bibliography  Save this article

Power and Type I error rates of goodness-of-fit statistics for binomial generalized estimating equations (GEE) models

Author

Listed:
  • Lin, Hui-Yi
  • Myers, Leann

Abstract

No abstract is available for this item.

Suggested Citation

  • Lin, Hui-Yi & Myers, Leann, 2006. "Power and Type I error rates of goodness-of-fit statistics for binomial generalized estimating equations (GEE) models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3432-3448, August.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:12:p:3432-3448
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(05)00164-7
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Wei Pan, 2002. "Goodness‐of‐fit Tests for GEE with Correlated Binary Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 101-110, March.
    2. Wei Pan, 2001. "Model Selection in Estimating Equations," Biometrics, The International Biometric Society, vol. 57(2), pages 529-534, June.
    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. Lin, Kuo-Chin, 2010. "Goodness-of-fit tests for modeling longitudinal ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1872-1880, July.

    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. Shinpei Imori, 2015. "Model Selection Criterion Based on the Multivariate Quasi-Likelihood for Generalized Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1214-1224, December.
    2. Blommaert, A. & Hens, N. & Beutels, Ph., 2014. "Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 667-680.
    3. Marc Aerts & Niel Hens & Jeffrey Simonoff, 2010. "Model selection in regression based on pre-smoothing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1455-1472.
    4. Lan Wang & Annie Qu, 2009. "Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 177-190, January.
    5. Andreas Blöchlinger & Markus Leippold, 2011. "A New Goodness-of-Fit Test for Event Forecasting and Its Application to Credit Defaults," Management Science, INFORMS, vol. 57(3), pages 487-505, March.
    6. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.
    7. Kuo-Chin Lin & Yi-Ju Chen, 2016. "Goodness-of-fit tests of generalized linear mixed models for repeated ordinal responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 2053-2064, August.
    8. M. C. Pardo & R. Alonso, 2012. "A generalized Q--Q plot for longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2349-2362, July.
    9. Lin, Kuo-Chin, 2010. "Goodness-of-fit tests for modeling longitudinal ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1872-1880, July.
    10. Daniel O. Scharfstein & Rafael A. Irizarry, 2003. "Generalized Additive Selection Models for the Analysis of Studies with Potentially Nonignorable Missing Outcome Data," Biometrics, The International Biometric Society, vol. 59(3), pages 601-613, 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:eee:csdana:v:50:y:2006:i:12:p:3432-3448. 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/locate/csda .

    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.