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

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Author Info
Carling, Kenneth () (Department of Business, Economics, Statistics and Informatics)
Alam, Moudud () (Department of Business, Economics, Statistics and Informatics)
Abstract

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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Paper provided by Örebro University, Swedish Business School in its series Working Papers with number 2007:14.

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Length: 22 pages
Date of creation: 10 Sep 2007
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Handle: RePEc:hhs:oruesi:2007_014

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Related research
Keywords: Monte-Carlo simulations; large sample; interdependence; cluster error;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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). [Downloadable!]
  2. 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. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
  5. 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. [Downloadable!] (restricted)
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