Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

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 ...

Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...

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 ...

Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse ...

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 ...