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VOL. 49 | 2006 Regression tree models for designed experiments


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.


Published: 1 January 2006
First available in Project Euclid: 28 November 2007

zbMATH: 1268.62090
MathSciNet: MR2337836

Digital Object Identifier: 10.1214/074921706000000464

Primary: 60K35 , 62K15
Secondary: 62G08

Keywords: AIC , ANOVA , Factorial , interaction , logistic , Poisson

Rights: Copyright © 2006, Institute of Mathematical Statistics


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