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Computationally feasible estimation of the covariance structure in Generalized linear mixed models(GLMM)

  • Carling, Kenneth

    ()

    (Department of Business, Economics, Statistics and Informatics)

  • Alam, Moudud

    ()

    (Department of Business, Economics, Statistics and Informatics)

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    In this paper we discuss how a regression model, with a non-continuous response variable, that allows for dependency between observations should be estimated when observations are clustered and there are repeated measurements on the subjects. The cluster sizes are assumed to be large. We …nd that the conventional estimation technique suggested by the literature on Generalized Linear Mixed Models (GLMM) is slow and often fails due to non-convergence and lack of memory on standard PCs. We suggest to estimate the random e¤ects as …xed e¤ects by GLM and derive the covariance matrix from these estimates. A simulation study shows that our proposal is feasible in terms of Mean-Square Error and computation time. We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal depending on the size of the clusters.

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    File URL: http://www.oru.se/PageFiles/15374/WP2007-14.pdf
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    Paper provided by Örebro University, School of Business in its series Working Papers with number 2007:14.

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    Length: 22 pages
    Date of creation: 10 Sep 2007
    Date of revision:
    Handle: RePEc:hhs:oruesi:2007_014
    Contact details of provider: Postal: Örebro University School of Business, SE - 701 82 ÖREBRO, Sweden
    Phone: 019-30 30 00
    Fax: 019-33 25 46
    Web page: http://www.oru.se/Institutioner/Handelshogskolan-vid-Orebro-universitet/

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    1. Yun, Sungcheol & Lee, Youngjo, 2004. "Comparison of hierarchical and marginal likelihood estimators for binary outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 639-650, April.
    2. William Greene, 2004. "The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 98-119, 06.
    3. Guilkey, David K. & Murphy, James L., 1993. "Estimation and testing in the random effects probit model," Journal of Econometrics, Elsevier, vol. 59(3), pages 301-317, October.
    4. Quintana, Fernando A. & Liu, Jun S. & Pino, Guido E. del, 1999. "Monte Carlo EM with importance reweighting and its applications in random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 429-444, February.
    5. G. S. Maddala, 1987. "Limited Dependent Variable Models Using Panel Data," Journal of Human Resources, University of Wisconsin Press, vol. 22(3), pages 307-338.
    6. James J. Heckman & Robert J. Willis, 1976. "Estimation of a Stochastic Model of Reproduction: An Econometric Approach," NBER Chapters, in: Household Production and Consumption, pages 99-146 National Bureau of Economic Research, Inc.
    7. Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series 168, Sveriges Riksbank (Central Bank of Sweden).
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