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Zbl 0889.62061
McCulloch, Charles E.
Maximum likelihood algorithms for generalized linear mixed models. (English)
[J] J. Am. Stat. Assoc. 92, No.437, 162-170 (1997). ISSN 0162-1459; ISSN 1537-274X/e

Summary: Maximum likelihood algorithms are described for generalized linear mixed models. I show how to construct a Monte Carlo version of the EM algorithm, propose a Monte Carlo Newton-Raphson algorithm, and evaluate and improve the use of importance sampling ideas. Calculation of the maximum likelihood estimates is feasible for a wide variety of problems where they were not previously. I also use the Newton-Raphson algorithm as a framework to compare maximum likelihood to the ``joint-maximization'' or penalized quasi-likelihood methods and explain why the latter can perform poorly.

MSC 2000:: *62J12 Generalized linear models
65C99 Numerical simulation
62F10 Point estimation
65C05 Monte Carlo methods

Keywords: importance sampling; Metropolis-Hastings algorithm; Monte Carlo EM; penalized quasi-likelihood; simulated maximum likelihood; Newton-Raphson algorithm

Cited in: Zbl 1194.62060

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Zentralblatt MATH Berlin [Germany]