Generalized Additive Models: An Introduction with R (2nd Edition)

By V. Gómez‐Rubio. 2018

546 citations recorded by the source corpus.

Generalized additive models (GAMs) are one of the main modeling tools for data analysis. GAMs can efficiently combine different types of fixed, random and smooth terms in the linear predictor of a regression model to account for different types of effects. Then this linear predictor can be conveniently linked to the mean of the observations, that are modeled using a distribution from the exponential family. As described in Wood’s book, GAMs cover a wide range of statistical models used in practice, such as the general linear model, generalized linear models and mixed-effects models. This is stressed throughout the book with numerous examples.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/025f25133a5c1da746eb7e7719bb715b71a7f518

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. David L. Miller
2. G. Simpson
3. Eric J. Pedersen
4. Noam Ross

In Statistics and Probability

1. David L. Miller
2. G. Simpson
3. Eric J. Pedersen
4. Noam Ross

Citations in the corpus

• Hierarchical generalized additive models in ecology: an introduction with mgcv (2019, methodology): “It can be interpreted similarly to the residual sum of squares for a simple linear regression ( Wood, 2017a , p. 109).”