Arbeitspapier
Asymptotically Optimal Regression Trees
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.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 2018:12
- Classification
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
- Subject
-
Piece-Wise Linear Regression
Partitioning Estimators
Non-Parametric Regression
Categorization
Partition
Prediction Trees
Decision Trees
Regression Trees
Regressogram
Mean Squared Error
- Event
-
Geistige Schöpfung
- (who)
-
Mohlin, Erik
- Event
-
Veröffentlichung
- (who)
-
Lund University, School of Economics and Management, Department of Economics
- (where)
-
Lund
- (when)
-
2018
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Mohlin, Erik
- Lund University, School of Economics and Management, Department of Economics
Time of origin
- 2018
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