The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth ...

We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

This is a preview. Log in through your library . Abstract Generalized additive models are a popular class of multivariate non-parametric regression models, due in large part to the ease of use of the ...

We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the ...

Abstract: While machine learning methods have significantly improved model performance over traditional methods, their black-box structure makes it difficult for researchers to interpret results. For ...

Abstract: Generalized additive models (GAMs) have been successfully applied to high dimensional data. However, most existing methods cannot capture the high level feature patterns from complex data.