A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering ...

The model aims to estimate the linear relationship between the location expression and cellular expression of specific marker genes, where the generalized L2 norm is induced by the graph Laplacian ...

Analytical expressions are available only for linear models and are provided in an appendix. For other multilevel generalized linear models we present approximations and suggest using parametric ...

Enhance your data analysis skills with this hands-on course on Generalised Linear Models in R. Learn to model complex data types and apply GLMs in real-world scenarios.

The paper introduces the PILOT learning algorithm for constructing linear model trees, enhancing decision tree interpretability and performance. It uses a standard regression model with centered ...

Objectives To model trajectories of antenatal and postnatal growth using linear spline multilevel models. Design Prospective cohort study. Setting Maternity hospital in Dublin, Ireland. Participants ...

The following solutions were proposed: i) Classical linear model with data transformation and ii) Generalized linear mixed models. The assumptions of normality and homogeneity were tested by ...

Generalized linear models (GLMs) are a widely utilized family of machine learning models in real-world applications. As data size increases, it is essential to perform efficient distributed training ...

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