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• W1614890185 abstract "<!-- *** Custom HTML *** --> 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." @default.
• W1614890185 created "2016-06-24" @default.
• W1614890185 creator A5080152913 @default.
• W1614890185 date "2006-01-01" @default.
• W1614890185 modified "2023-09-27" @default.
• W1614890185 title "Regression tree models for designed experiments" @default.
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• W1614890185 doi "https://doi.org/10.1214/074921706000000464" @default.
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