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In [[statistics]], an '''additive model''' ('''AM''') is a [[nonparametric regression]] method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) and is an essential part of the [[ACE]]{{Disambiguation needed|date=January 2012}} algorithm. The ''AM'' uses a one dimensional [[Smoothing|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 [[linear regression|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==
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==References==
*Buja, A., Hastie, T., and Tibshirani, R. (1989). "Linear Smoothers and Additive Models", ''The Annals of Statistics'' 17(2):453–555.
*Breiman, L. and Friedman, J.H. (1985). "Estimating Optimal Transformations for Multiple Regression and Correlation", ''[[Journal of the American Statistical Association]]'' 80:580–598.
*Friedman, J.H. and Stuetzle, W. (1981. "Projection Pursuit Regression", ''Journal of the American Statistical Association'' 76:817–823
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