Maximum Likelihood Regression Trees
نویسنده
چکیده
We put forward a new method of growing regression trees via maximum likelihood. It inherits the CART (Brieman et al., 1984) backward fitting idea. However, standard likelihood based methods such as model selection criteria and likelihood ratio tests are naturally incorporated into each stage of the tree procedure. Compared with other least squared tree methods, maximum likelihood regression trees (MLRT) reject the use of many ad hoc approaches and rely on more established methods; they have easy extension to handle data involving other types of responses; in addition, simulation study shows that MLRT tends to provide more accurate tree size selection than CART. The analysis of the 1992 baseball salary data is given as an illustration.
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