Abstract
The models fit in Chap. 2 have two limitations. First, the conditional distribution of the response, given the predictors, is assumed to be Gaussian. Second, only a single predictor is allowed to have a smooth nonlinear effect—the other predictors are modeled linearly. The first limitation is addressed by using generalized linear models (GLMs), which remove the Gaussian assumption and allow the response variable to have other distributions such as those within the Binomial and Poisson families.
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Harezlak, J., Ruppert, D., Wand, M.P. (2018). Generalized Additive Models. In: Semiparametric Regression with R. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8853-2_3
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