Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Maximum Likelihood Estimation Using Laplace Approximation in Poisson GLMMs

  • Ha, Il-Do (Department of Asset Management, Daegu Haany University)
  • Received : 20090700
  • Accepted : 20090900
  • Published : 2009.11.30
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Abstract

Poisson generalized linear mixed models(GLMMs) have been widely used for the analysis of clustered or correlated count data. For the inference marginal likelihood, which is obtained by integrating out random effects is often used. It gives maximum likelihood(ML) estimator, but the integration is usually intractable. In this paper, we propose how to obtain the ML estimator via Laplace approximation based on hierarchical-likelihood (h-likelihood) approach under the Poisson GLMMs. In particular, the h-likelihood avoids the integration itself and gives a statistically efficient procedure for various random-effect models including GLMMs. The proposed method is illustrated using two practical examples and simulation studies.

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References

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