Additive model
In statistics, an additive model is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle and is an essential part of the ACE algorithm. The AM uses a one-dimensional smoother to build a restricted class of nonparametric regression models. Because of this, it is less affected by the curse of dimensionality than e.g. a p-dimensional smoother. Furthermore, the AM is more flexible than a standard linear model, while being more interpretable than a general regression surface at the cost of approximation errors. Problems with AM include model selection, overfitting, and multicollinearity.Description
Given a data set of n statistical units, where represent predictors and is the outcome, the additive model takes the form
or
Where, and. The functions are unknown smooth functions fit from the data. Fitting the AM can be done using the backfitting algorithm proposed by Andreas Buja, Trevor Hastie and Robert Tibshirani.
