Multiple additive regression trees with application in epidemiology

Predicting future outcomes based on knowledge obtained from past observational data is a common application in a wide variety of areas of scientific research. In the present paper, prediction will be focused on various grades of cervical preneoplasia and neoplasia. Statistical tools used for prediction should of course possess predictive accuracy, and preferably meet secondary requirements such as speed, ease of use, and interpretability of the resulting predictive model. A new automated procedure based on an extension (called 'boosting') of regression and classification tree (CART) models is described. The resulting tool is a fast 'off-the-shelf' procedure for classification and regression that is competitive…