Conditional inference about generalized linear mixed models
The Annals of Statistics · 1999 · Volume 27 · Issue 6
Abstract
We propose a method of inference for generalized linear mixed models (GLMM) that in many ways resembles the method of least squares. We also show that adequate inference about GLMM can be made based on the conditional likelihood on a subset of the random effects. One of the important features of our methods is that they rely on weak distributional assumptions about the random effects. The methods proposed are also computationally feasible. Asymptotic behavior of the estimates is investigated. In particular, consistency is proved under reasonable conditions.
Keywords
62F12, 62J12, Asymptotic properties of estimates, maximum conditional likelihood, penalized generalized WLS, semiparametric inference
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