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A nonlinear Gauss-Seidel algorithm for inference about GLMM

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Summary

A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed model. We show that the algorithm converges in virtually all typical situations of generalized linear mixed models. A numerical example shows the superiority of the proposed algorithm over the standard Newton-Raphson procedure when the number of random effects is large.

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Acknowledgement

The author wishes to thank Professor Zhen Luo for his helpful suggestion. The author is also grateful to a referee whose constructive comments lead to consideration of the numerical example in the paper.

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Authors and Affiliations

  1. Department of Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-7054, USA

    Jiming Jiang

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  1. Jiming Jiang
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Supported in part by NSA Grant MDA904-98-1-0038.

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Jiang, J. A nonlinear Gauss-Seidel algorithm for inference about GLMM. Computational Statistics 15, 229–241 (2000). https://doi.org/10.1007/s001800000030

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  • DOI: https://doi.org/10.1007/s001800000030

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