Arbeitspapier

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

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.

Language
Englisch

Bibliographic citation
Series: Working Paper ; No. 14/2007

Classification
Wirtschaft
Estimation: General
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Computational Techniques; Simulation Modeling
Subject
Monte-Carlo simulations
large sample
interdependence
cluster error

Event
Geistige Schöpfung
(who)
Carling, Kenneth
Alam, Moudud
Event
Veröffentlichung
(who)
Örebro University School of Business
(where)
Örebro
(when)
2007

Handle
Last update
12.03.2025, 6:42 PM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Carling, Kenneth
  • Alam, Moudud
  • Örebro University School of Business

Time of origin

  • 2007

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