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

Abstract: We compared the goodness of fit and efficiency of models for germination. Generalized Linear Models (GLMs) were performed with a randomized component corresponding to the percentage of ...

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

In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models ...

Different types of multivariable analysis: generalized linear models Generalized linear models (GLMs, table 2) are a flexible and powerful class of statistical models widely used in multivariable ...

We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled ...

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 ...

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

Sparse generalized linear model is useful in many fields. In the research, the researchers will learn sparse generalized linear model using different algorithms. The paper determines the better ...