Regression tree models for designed experiments

Details Regression tree models for designed experiments Regression tree models for designed experiments

2006-11-07

arxiv.org/abs/math/0611192v1

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.

Affiliated with

Also associated with

No associations

Rating

Regression tree models for designed experiments does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Regression tree models for designed experiments, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Regression tree models for designed experiments will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-617880