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2016 | OriginalPaper | Chapter

16. Generalized Additive Modeling

Authors : George J. Knafl, Kai Ding

Published in: Adaptive Regression for Modeling Nonlinear Relationships

Publisher: Springer International Publishing

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Abstract

This chapter formulates and demonstrates generalized additive models (GAMs) for means of continuous outcomes treated as independent and normally distributed with constant variances as in linear regression and for logits (log odds) of means of dichotomous discrete outcomes with unit dispersions as in logistic regression. GAMs provide an alternative to fractional polynomial models for modeling nonlinear relationships between univariate outcomes and predictors, and so GAMs for these two cases are also compared to adaptive fractional polynomial models. Poisson regression is not considered for brevity. Example analyses are provided of the univariate continuous outcome deathrate per 100,000 in terms of available predictors as also addressed in Chaps. 2, 3, 6 and 7 as well as the univariate dichotomous outcome a high mercury level in fish over 1.0 ppm versus a lower level in terms of available predictors as also addressed in Chaps. 8 and 9.

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Literature
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Metadata
Title
Generalized Additive Modeling
Authors
George J. Knafl
Kai Ding
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-33946-7_16

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