IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v69y2013i3p633-640.html
   My bibliography  Save this article

GEE for Multinomial Responses Using a Local Odds Ratios Parameterization

Author

Listed:
  • Anestis Touloumis
  • Alan Agresti
  • Maria Kateri

Abstract

No abstract is available for this item.

Suggested Citation

  • Anestis Touloumis & Alan Agresti & Maria Kateri, 2013. "GEE for Multinomial Responses Using a Local Odds Ratios Parameterization," Biometrics, The International Biometric Society, vol. 69(3), pages 633-640, September.
  • Handle: RePEc:bla:biomet:v:69:y:2013:i:3:p:633-640
    DOI: 10.1111/biom.12054
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12054
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1111/biom.12054?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. N. Rao Chaganty & Harry Joe, 2006. "Range of correlation matrices for dependent Bernoulli random variables," Biometrika, Biometrika Trust, vol. 93(1), pages 197-206, March.
    2. N. Rao Chaganty & Harry Joe, 2004. "Efficiency of generalized estimating equations for binary responses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 851-860, November.
    3. Becker, Mark P., 1989. "On the bivariate normal distribution and association models for ordinal categorical data," Statistics & Probability Letters, Elsevier, vol. 8(5), pages 435-440, October.
    4. You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
    5. N. R. Parsons & R. N. Edmondson & S. G. Gilmour, 2006. "A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 507-524, August.
    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. Dale Bowman & E. Olusegun George, 2017. "Weighted least squares estimation for exchangeable binary data," Computational Statistics, Springer, vol. 32(4), pages 1747-1765, December.
    2. J. Perin & J. S. Preisser & C. Phillips & B. Qaqish, 2014. "Regression analysis of correlated ordinal data using orthogonalized residuals," Biometrics, The International Biometric Society, vol. 70(4), pages 902-909, December.
    3. Ronald Herrera & Ursula Berger & Jon Genuneit & Jessica Gerlich & Dennis Nowak & Wolff Schlotz & Christian Vogelberg & Erika Von Mutius & Gudrun Weinmayr & Doris Windstetter & Matthias Weigl & Katja R, 2017. "Chronic Stress in Young German Adults: Who Is Affected? A Prospective Cohort Study," IJERPH, MDPI, vol. 14(11), pages 1-13, October.
    4. Thomas, Robert D. & Davis, John W. & Cuccaro, Paula M. & Gemeinhardt, Gretchen L., 2022. "Assessing associations between insecure income and US workers’ health: An IPUMS-MEPS analysis," Social Science & Medicine, Elsevier, vol. 309(C).
    5. Yuqi Tian & Bryan E. Shepherd & Chun Li & Donglin Zeng & Jonathan S. Schildcrout, 2023. "Analyzing clustered continuous response variables with ordinal regression models," Biometrics, The International Biometric Society, vol. 79(4), pages 3764-3777, December.
    6. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    7. Daniel Fernández & Louise McMillan & Richard Arnold & Martin Spiess & Ivy Liu, 2022. "Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model," Stats, MDPI, vol. 5(2), pages 1-14, June.
    8. Alan Agresti, 2014. "Two Bayesian/frequentist challenges for categorical data analyses," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 125-132, August.
    9. Alan Agresti & Maria Kateri, 2019. "The class of CUB models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 445-449, September.
    10. Gómez Silva Carlos Alberto, 2016. "Clasificación de colegios según las pruebas Saber 11 del ICFES: un análisis usando modelos marginales (MM)," Revista Sociedad y Economía, Universidad del Valle, CIDSE, vol. 0(30), pages 11-404, January.
    11. Carlos Alberto GÓMEZ SILVA, 2014. "Clasificación de colegios según las Pruebas SABER 11 del ICFES en el Período 2001-2011: un Análisis Longitudinal a Través del Uso de Modelos Marginales (MM)," Archivos de Economía 12314, Departamento Nacional de Planeación.
    12. Gem Stapleton & Peter Chapman & Peter Rodgers & Anestis Touloumis & Andrew Blake & Aidan Delaney, 2019. "The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-25, March.

    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. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    2. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
    3. Peng, Cheng & Yang, Yihe & Zhou, Jie & Pan, Jianxin, 2022. "Latent Gaussian copula models for longitudinal binary data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Alessandro Barbiero & Asmerilda Hitaj, 2020. "Goodman and Kruskal’s Gamma Coefficient for Ordinalized Bivariate Normal Distributions," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 905-925, December.
    5. Fu, Liya & Wang, You-Gan, 2016. "Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 492-502.
    6. Wang, You-Gan & Hin, Lin-Yee, 2010. "Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3359-3370, December.
    7. 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.
    8. Deng, Yihao & Sabo, Roy T. & Chaganty, N. Rao, 2012. "Multivariate probit analysis of binary familial data using stochastic representations," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 656-663.
    9. Fontana, Roberto & Semeraro, Patrizia, 2018. "Representation of multivariate Bernoulli distributions with a given set of specified moments," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 290-303.
    10. Roy T. Sabo & N. Rao Chaganty, 2011. "Letter to the Editor of Biometrics on “Joint Regression Analysis for Discrete Longitudinal Data” by Madsen and Fang," Biometrics, The International Biometric Society, vol. 67(4), pages 1669-1670, December.
    11. Oman, Samuel D., 2009. "Easily simulated multivariate binary distributions with given positive and negative correlations," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 999-1005, February.
    12. Samuel D. Oman & Victoria Landsman & Yohay Carmel & Ronen Kadmon, 2007. "Analyzing Spatially Distributed Binary Data Using Independent-Block Estimating Equations," Biometrics, The International Biometric Society, vol. 63(3), pages 892-900, September.
    13. Parsons, Nick R. & Costa, Matthew L. & Achten, Juul & Stallard, Nigel, 2009. "Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 632-641, January.
    14. Højsgaard, Søren & Halekoh, Ulrich & Yan, Jun, 2005. "The R Package geepack for Generalized Estimating Equations," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i02).
    15. Liya Fu & Zhuoran Yang & Yan Zhou & You-Gan Wang, 2021. "An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 679-709, October.
    16. Feng, Sanying & Lian, Heng & Xue, Liugen, 2016. "A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 98-109.
    17. Tang, Niansheng & Wang, Wenjun, 2019. "Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 640-655.
    18. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    19. 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.
    20. You-Gan Wang & Yuning Zhao, 2007. "A Modified Pseudolikelihood Approach for Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 63(3), pages 681-689, 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:69:y:2013:i:3:p:633-640. 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.