Asymptotically Optimal Regression Trees - Erik Mohlin (Lund University, School of Economics and Management, Department of Economics, 2018)
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.
Keywords: C14; C38; 330; Piece-Wise Linear Regression; Partitioning Estimators; Non-Parametric Regression; Categorization; Partition; Prediction Trees; Decision Trees; Regression Trees; Regressogram; Mean Squared Error