Linear regression

Linear model

Engineering

Random effects model

Statistics

Materials science

Applied mathematics

Range (aeronautics)

Computer science

Additive model

Generalized linear mixed model

Mechanical engineering

Mathematics

Exponential function

Generalized linear model

Meta-analysis

Internal medicine

Mathematical analysis

Exponential family

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Medicine

Cover (algebra)

Generalized additive model

Journal of Statistical Software

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.

Generalized Additive Models: An Introduction with <i>R</i> (2nd Edition)

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