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Linear and generalized linear mixed models
2015AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
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On Inverse Prediction in Mixed Linear Models
Communications in Statistics - Simulation and Computation, 2014Given training data, a model relating a multivariate response y to x, and y* from a mystery specimen, the objective is to infer what values x* might have given rise to y*. Two approaches are investigated and illustrated here. In one, inverse prediction, tenable values of x* are those at which y* does not test as an outlier.
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Linear Mixed Models for Longitudinal Data
2000Verbeke, Geert, Molenberghs, Geert
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