Resumen de A computationally efficient estimator for large clustered non-Gaussian data
Alvaro J. Flórez, Geert Molenberghs, Geert Verbeke, Pavlos Mamouris, Bert Vaes
The generalized linear mixed model (GLMM) is one of the most frequently used techniques to analyze clustered non-Gaussian data. Commonly, the GLMM is fitted by maximizing the marginal (log-)likelihood, i.e., integrating out the random effects. However, this whole maximisation may require a considerable amount of computing resources. Although computationally manageable with medium to large data, it can be too time-consuming or computationally intractable with very large clusters and/or with a large number of clusters. To overcome this, a fast two-stage estimator for correlated non-Gaussian data is presented. It is rooted in the pseudo-likelihood split-sample methodology. Based on simulations, it shows good statistical properties, and it is computationally much faster than full maximum likelihood. The approach is illustrated using a large dataset belonging to a network of Belgian general practices
