Generalised Linear (GLM), Nonlinear (NLGLM) And General Additive Models (GAM) (GNAM02)
25 May 2020 - 29 May 2020
This course provides a general introduction to nonlinear regression analysis, covering major topics including, but not limited to, general and generalized linear models, generalized additive models, spline and radial basis function regression, and Gaussian process regression. We approach the general topic of nonlinear regression by showing how the powerful and flexible statistical modelling framework of general and generalized linear models, and their multilevel counterparts, can be extended to handle nonlinear relationships between predictor and outcome variables. We begin by providing a comprehensive practical and theoretical overview of regression, including multilevel regression, using general and generalized linear models. Here, we pay particular attention to the many variants of general and generalized linear models, and how these provide a very widely applicable set of tools for statistical modeling. After this introduction, we then proceed to cover practically and conceptually simple extensions to the general and generalized linear models framework using parametric nonlinear models and
polynomial regression. We will then cover more powerful and flexible extensions of this modeling. framework by way of the general concept of basis functions. We’ll begin our coverage of basis function regression with the major topic of spline regression, and then proceed to cover radial basis functions and the multilayer perceptron, both of which are types of artificial neural networks. We then move on to the major topic of generalized additive models (GAMs) and generalized additive mixed models (GAMMs), which can be viewed as the generalization of all the basis function regression topics, but cover a wider range of topic including nonlinear spatial and temporal models and interaction models. Finally, we will cover the powerful Bayesian nonlinear regression method of Gaussian process regression.
Full details can be found using the link below