GLMbasierte

GLMbasierte is a term that generally refers to statistical models based on the Generalized Linear Model (GLM) framework. The GLM is a flexible generalization of ordinary least squares regression that allows for response variables that have error distribution models other than a normal distribution, and for the response variable's mean to depend on a linear predictor through a link function.

The GLM framework allows for a wide variety of statistical models to be unified under a single structure. This includes linear regression for normally distributed data, logistic regression for binary data, Poisson regression for count data, and gamma regression for positive, continuous data. The key components of a GLM are the random component (the probability distribution of the response variable), the systematic component (the linear predictor), and the link function, which connects the mean of the distribution to the linear predictor.

When something is described as "GLMbasierte," it implies that the underlying statistical methodology or analysis is built upon this foundational GLM structure. This means that the model assumes a specific distribution for the response variable, a linear relationship between predictor variables and a transformed version of the response variable's mean, and that this transformation is defined by a link function. Understanding the specific distribution and link function used is crucial for interpreting the results of a GLMbasierte analysis.