The Bayesian Additive Classification Tree applied to credit risk modelling
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Other versions of this item:
- Junni L. Zhang & Wolfgang Härdle, 2008. "The Bayesian Additive Classification Tree Applied to Credit Risk Modelling," SFB 649 Discussion Papers SFB649DP2008-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
References listed on IDEAS
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
- Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007.
"Estimating probabilities of default with support vector machines,"
Discussion Paper Series 2: Banking and Financial Studies
2007,18, Deutsche Bundesbank.
- Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2007. "Estimating Probabilities of Default With Support Vector Machines," SFB 649 Discussion Papers SFB649DP2007-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Wolfgang Karl HÃ¤rdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
More about this item
KeywordsClassification and regression tree Financial ratio Misclassification rate Accuracy ratio;
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
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