McCulloch, Charles E. Maximum likelihood algorithms for generalized linear mixed models. (English) Zbl 0889.62061 J. Am. Stat. Assoc. 92, No. 437, 162-170 (1997). 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. Cited in 1 ReviewCited in 183 Documents MSC: 62J12 Generalized linear models (logistic models) 65C99 Probabilistic methods, stochastic differential equations 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 PDFBibTeX XMLCite \textit{C. E. McCulloch}, J. Am. Stat. Assoc. 92, No. 437, 162--170 (1997; Zbl 0889.62061) Full Text: DOI Link