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Abstract: This article describes flexible statistical methods that may be used to identify and characterize nonlinear regression effects. These methods are called "generalized additive models". For example, a commonly used statistical model in medical research is the logistic regression model for binary data. Here we relate the mean of the binary response ¯ = P (y = 1) to the predictors via a linear regression model and the logit link function: log (Update)
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BibTeX entry: (Update)
Hastie, T., Tibshirani, R. (1990): Generalized additive models. Chapman and Hall, London. http://citeseer.ist.psu.edu/hastie95generalized.html More
@article{ hastie86generalized,
author = "T. Hastie and R. Tibshirani",
title = "Generalized Additive Models",
journal = "Statistical Science",
volume = "1",
pages = "297--318",
year = "1986",
url = "citeseer.ist.psu.edu/hastie95generalized.html" }
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