Generalized Additive Models: An Introduction With R

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Key Points

Introduce the foundational theory, estimation mechanics, and practical computational applications of generalized additive models using the R statistical environment.
Reviews the mathematical principles of penalized regression splines, generalized linear models, and smoothness selection criteria.
Outlines implementation workflows using specialized statistical packages in the R computing environment for fitting flexible smooth functions.
Extends standard linear and generalized linear regression by allowing predictor effects to be modeled as unspecified smooth non-linear functions.
Demonstrates reliable parameter estimation and computational efficiency when selecting smoothing parameters to prevent overfitting in complex data sets.

Methodological review demonstrates generalized additive modeling frameworks in statistical software, highlighting non-linear estimation techniques.

"Generalized Additive Models: An Introduction With R." Journal of the American Statistical Association, 102(478), pp. 760–761

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Christine M. Anderson‐Cook (2007) studied this question.

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