Additive Modeling (AM) Algorithm

An Additive Modeling (AM) Algorithm is a nonparametric regression algorithm in which the combined effect of the explanatory variables (and their interaction) is equal to the Sum of their separate effects.

• Example(s):
  • Generalized Additive Model.
  • one proposed in (Friedman & Stuetzle, 1981).
• See: Additive Tree, Alternating Conditional Expectation Model, Smoothing, Curse of Dimensionality, Multicollinearity, Model Interpretability.

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References

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Additive_model Retrieved:2017-10-17.
  • 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 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.

2009

• (Hastie et al., 2009) ⇒ Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman. (2009). “The Elements of Statistical Learning: Data Mining, Inference, and Prediction; 2nd edition.” Springer-Verlag. ISBN:0387848576

2008

• (Upton & Cook, 2008) ⇒ Graham Upton, and Ian Cook. (2008). “A Dictionary of Statistics, 2nd edition revised.” Oxford University Press. ISBN:0199541450
  • QUOTE: A model in which the combined effect of the explanatory variables (and their interaction) is equal to the Sum of their separate effects.

1981

• (Friedman & Stuetzle, 1981) ⇒ Jerome H. Friedman and W. Stuetzle. (1981). “Projection Pursuit Regression", Journal of the American Statistical Association, 76. doi:10.1080/01621459.1981.10477729

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