Statistics - Generalized Linear Models (GLM) - Extensions of the Linear Model

1 - About

The Generalized Linear Model allows for lots of different, non-linear models to be tested in the context of regression.

GLM is the mathematical framework used in many statistical analyses such as:

GLM is a supervised algorithm with a classic statistical technique (Supports thousands of input variables, text and transactional data) used for:

GLM implements:

Confidence bounds are supported with a

  • GLM classification for prediction probabilities.
  • GLM regression for predictions.

3 - Assumptions

The General Linear model has two main characteristics:

  • Linear: linear relationships between the predictors and the outcome measure.
  • Additive: the effects of each predictor are additive with one another

That doesn't mean that the GLM can't handle non-additive or non-linear effects.

Removing the additive assumption:

GLM can accommodate such non-additive or non-linear effects with:

  • Transformation of variables: in order to make them linear
  • Adding interaction terms or moderation terms: in order to do a moderation analysis and test for non-additive facts.

4 - Methods

Methods that expand the scope of linear models and how they are fit:

  • Classification problems: logistic regression, support vector machines
  • Non-linearity: kernel smoothing, splines and generalized additive models; nearest neighbour methods.
  • Interactions: Tree-based methods, bagging, random forests and boosting (these also capture non-linearities)
  • Regularized fitting: Ridge regression and lasso. These have become very popular lately, especially when we have data sets where we have very large numbers of variables–so-called wide data sets, and even linear models are too rich for them, and so we need to use methods to control the variability.
data_mining/glm.txt · Last modified: 2017/11/16 22:50 by gerardnico