Mathematics – Statistics Theory
Scientific paper
2006-11-07
IMS Lecture Notes--Monograph Series 2006, Vol. 49, 210-228
Mathematics
Statistics Theory
Published at http://dx.doi.org/10.1214/074921706000000464 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/
Scientific paper
10.1214/074921706000000464
Although regression trees were originally designed for large datasets, they can profitably be used on small datasets as well, including those from replicated or unreplicated complete factorial experiments. We show that in the latter situations, regression tree models can provide simpler and more intuitive interpretations of interaction effects as differences between conditional main effects. We present simulation results to verify that the models can yield lower prediction mean squared errors than the traditional techniques. The tree models span a wide range of sophistication, from piecewise constant to piecewise simple and multiple linear, and from least squares to Poisson and logistic regression.
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