Additive model: Difference between revisions

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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)<ref>[[Friedman, J.H.]] and Stuetzle, W. (1981). "Projection Pursuit Regression", ''Journal of the American Statistical Association'' 76:817&ndash;823. {{doi|10.1080/01621459.1981.10477729}}</ref> and is an essential part of the [[Alternating conditional expectation model|ACE]] 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==