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 ...

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K})$ is possibly much larger than the ...

Although this model is not the focus of the course, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models. Topic 2: Binary ...

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

In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...

Abstract: With the development of the artificial intelligent technology, intelligent distribution load forecasting technology has been paid much attention. Based on the Generalized Linear Model, this ...

Abstract: Aim: The objective of the study is to improve the Accuracy and F-Measure percentage of At - risk students Model on the basis of High withdrawal and Failure rate and learning Achievement ...