Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260241 
Authors: 
Year of Publication: 
2018
Series/Report no.: 
Working Paper No. 2018:12
Publisher: 
Lund University, School of Economics and Management, Department of Economics, Lund
Abstract: 
Regression trees are evaluated with respect to mean square error (MSE), mean integrated square error (MISE), and integrated squared error (ISE), as the size of the training sample goes to infinity. The asymptotically MSE- and MISE minimizing (locally adaptive) regression trees are characterized. Under an optimal tree, MSE is O(n-2/3}). The estimator is shown to be asymptotically normally distributed. An estimator for ISE is also proposed, which may be used as a complement to cross-validation in the pruning of trees.
Subjects: 
Piece-Wise Linear Regression
Partitioning Estimators
Non-Parametric Regression
Categorization
Partition
Prediction Trees
Decision Trees
Regression Trees
Regressogram
Mean Squared Error
JEL: 
C14
C38
Document Type: 
Working Paper

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