Generalized Additive Models
May 1, 2024
4 minute read
Generalized Additive Models (GAMs) are a powerful and flexible class of statistical models that can be used to model nonlinear relationships between a response variable and one or more predictor variables. GAMs are an extension of generalized linear models (GLMs), which are themselves a generalization of linear regression models. However, unlike GLMs, GAMs allow the relationship between the response variable and the predictor variables to be nonlinear, which makes them well-suited for modeling a wide variety of data types.
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Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Generalized Additive Models.
Provides a comprehensive introduction to generalized additive models (GAMs) using the R statistical software. It covers the basics of GAMs, including their formulation, estimation, and interpretation, and provides practical guidance on how to use GAMs to analyze real-world data.
Provides a unified approach to statistical learning, including GAMs. It covers the basics of statistical learning, as well as more advanced topics such as model selection and regularization.
Provides a practical guide to the use of generalized additive models (GAMs) for statistical learning. It covers the basics of GAMs, including their formulation, estimation, and interpretation, and provides numerous examples of how GAMs can be used to solve real-world problems.
Provides a practical guide to using GAMs in R. It covers the basics of GAMs, as well as more advanced topics such as model selection and diagnostics.
Provides a practical guide to GAMs for scientific data analysis. It covers the basics of GAMs, as well as more advanced topics such as model selection and diagnostics.
Provides a comprehensive introduction to GAMs for count data. It covers the basics of GAMs, as well as more advanced topics such as model selection and inference.
Provides a comprehensive introduction to GAMs for longitudinal data. It covers the basics of GAMs, as well as more advanced topics such as model selection and inference.
Provides a user-friendly introduction to GAMs, with a focus on practical applications. It is written in a non-technical style, making it accessible to readers with a variety of backgrounds.
Provides an overview of the use of GAMs in ecology. It covers the basics of GAMs, as well as more advanced topics such as model selection and diagnostics.
Provides an overview of the use of GAMs in social science. It covers the basics of GAMs, as well as more advanced topics such as model selection and diagnostics.
Provides a gentle introduction to GAMs, with a focus on examples. It is written in a clear and concise style, making it accessible to readers with a variety of backgrounds.
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