Generalized Additive Models

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole.

How to extend linear regression to specify and estimate generalized linear models and additive models.

Learn to model with R: ANOVA, regression, GLMs, survival analysis, GAMs, mixed-effects, split-plot and nested designs

A mathematical journey through common machine learning frameworks in regression, classification, and clustering.

This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework.