This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of-fit analyses. Census tracts. Fire ...

A new and first of its kind book provides a practical guide for the use of modern statistical methods within agricultural and natural resources sciences. Analysis of Generalized Linear Mixed Models in ...

Interpretability has drawn increasing attention in machine learning. Partially linear additive models provide an attractive middle ground between the simplicity of generalized linear model and the ...

This is a preview. Log in through your library . Abstract The relationship between Potthoff & Roy's generalized linear model and Rao's conditional model is shown. Rao claimed that his method is more ...

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