Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed ...

These include the consideration of conditional versus marginal distributions and means, overdispersion (for discrete data), the model-fitting method [e.g., maximum likelihood (integral approximation), ...

Understanding the General Linear Model is essential for conducting rigorous statistical analyses, making informed inferences about relationships between variables, and developing predictive models.

So, Poisson regression was conducted for each transition count. Most statistical software packages conduct Poisson regression or generalized linear model utilizing log-link function with only one ...

Different from the conventional linear model, linear constraints are added to the proposed model to fulfill the continuity constraints of signals. To estimate the period of the signals, a hypothesis ...

This study proposes a constraint linear model (CLM) to represent periodic signals. Different from the conventional linear model, linear constraints are added to the proposed model to fulfill the ...

airGLMs is an R package for automatic iterative generalized linear model (GLM) selection. It uses a forward stepwise selection process, calling the glm function available in base R and using AIC or ...