IDEAS home Printed from https://ideas.repec.org/p/pqs/wpaper/0222005.html
   My bibliography  Save this paper

Asymptotic Results with Generalized Estimating Equations for Longitudinal data II

Author

Listed:
  • R. M. Balan

    (Department of Mathematics, University of Ottawa, LRSP)

  • Ioana Schiopu-Kratina

    (Statistics Canada)

Abstract

We consider the marginal models of Liang and Zeger (1986) for the analysis of longitudinal data and we develop a theory of statistical inference for such models. We prove the existence, weak consistency and asymptotic normality of a sequence of estimators defined as roots of pseudo-likelihood equations.

Suggested Citation

  • R. M. Balan & Ioana Schiopu-Kratina, 2004. "Asymptotic Results with Generalized Estimating Equations for Longitudinal data II," RePAd Working Paper Series lrsp-TRS398, Département des sciences administratives, UQO.
  • Handle: RePEc:pqs:wpaper:0222005
    as

    Download full text from publisher

    File URL: http://www.repad.org/ca/on/lrsp/TRS398.pdf
    File Function: First version, 2004
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lai, T. L. & Robbins, Herbert & Wei, C. Z., 1979. "Strong consistency of least squares estimates in multiple regression II," Journal of Multivariate Analysis, Elsevier, vol. 9(3), pages 343-361, September.
    2. Kaufmann, Heinz, 1987. "On the strong law of large numbers for multivariate martingales," Stochastic Processes and their Applications, Elsevier, vol. 26, pages 73-85.
    3. Yuan, Ke-Hai & Jennrich, Robert I., 1998. "Asymptotics of Estimating Equations under Natural Conditions," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 245-260, May.
    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. Dzhaparidze, K. & Spreij, P., 1989. "On SLLN for martingales with deterministic variation," Serie Research Memoranda 0079, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. Norbert Christopeit & Michael Massmann, 2013. "A Note on an Estimation Problem in Models with Adaptive Learning," Tinbergen Institute Discussion Papers 13-151/III, Tinbergen Institute.
    3. Küchler, Uwe & Sørensen, Michael M., 1998. "A note on limit theorems for multivariate martingales," SFB 373 Discussion Papers 1998,45, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Yuan-chin Chang, 2011. "Sequential estimation in generalized linear models when covariates are subject to errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(1), pages 93-120, January.
    5. Jonathan Schweig, 2014. "Multilevel Factor Analysis by Model Segregation," Journal of Educational and Behavioral Statistics, , vol. 39(5), pages 394-422, October.
    6. Boik, Robert J., 2008. "An implicit function approach to constrained optimization with applications to asymptotic expansions," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 465-489, March.
    7. Ke-Hai Yuan & Wai Chan & Yubin Tian, 2016. "Expectation-robust algorithm and estimating equations for means and dispersion matrix with missing data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 329-351, April.
    8. Zhou, Xian & You, Jinhong, 2004. "Wavelet estimation in varying-coefficient partially linear regression models," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 91-104, June.
    9. Kano, Yutaka & Takai, Keiji, 2011. "Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1241-1255, October.
    10. Chan, N.H. & Cheung, Simon K.C. & Wong, Samuel P.S., 2020. "Inference for the degree distributions of preferential attachment networks with zero-degree nodes," Journal of Econometrics, Elsevier, vol. 216(1), pages 220-234.
    11. Yong Tao & Xiangjun Wu & Tao Zhou & Weibo Yan & Yanyuxiang Huang & Han Yu & Benedict Mondal & Victor M. Yakovenko, 2019. "Exponential structure of income inequality: evidence from 67 countries," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 345-376, June.
    12. Jin Zhang, 2020. "Consistency of MLE, LSE and M-estimation under mild conditions," Statistical Papers, Springer, vol. 61(1), pages 189-199, February.
    13. Norbert Christopeit & Michael Massmann, 2010. "Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning," Tinbergen Institute Discussion Papers 10-077/4, Tinbergen Institute.
    14. Ke-Hai Yuan & Kentaro Hayashi, 2005. "On muthén’s maximum likelihood for two-level covariance structure models," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 147-167, March.
    15. Arnoud V. den Boer & Bert Zwart, 2014. "Simultaneously Learning and Optimizing Using Controlled Variance Pricing," Management Science, INFORMS, vol. 60(3), pages 770-783, March.
    16. Velilla Cerdan, Santiago, 1999. "Variable deletion conficence regions and bootstrapping in linear regression," DES - Working Papers. Statistics and Econometrics. WS 6351, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Feng, Jiarui & Zhu, Zhongyi, 2011. "Semiparametric analysis of longitudinal zero-inflated count data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 61-72, January.
    18. Yuan, Ke-Hai, 2009. "Normal distribution based pseudo ML for missing data: With applications to mean and covariance structure analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1900-1918, October.
    19. Ching-Kang Ing & Ching-Zong Wei, 2005. "A maximal moment inequality for long range dependent time series with applications to estimation and model selection," Econometrics 0508009, University Library of Munich, Germany.
    20. Zimu Chen & Zhanfeng Wang & Yuan‐chin Ivan Chang, 2020. "Sequential adaptive variables and subject selection for GEE methods," Biometrics, The International Biometric Society, vol. 76(2), pages 496-507, June.

    More about this item

    Keywords

    Generalized estimating equations; Generalized linear model; Consistency; Asymptotic normality;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pqs:wpaper:0222005. 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: Christian Calmes (email available below). General contact details of provider: https://edirc.repec.org/data/dsuqoca.html .

    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.