Application of bayesian additive regression trees in the development of credit scoring models in Brazil
Brito Filho, Daniel Alves de; Artes, Rinaldo
Abstract
Keywords
References
Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature.
Abellán, J., & Castellano, J. G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankrupt.
Anderson, R. (2007).
Bank for International Settlements – BIS. (2004).
Bank for International Settlements – BIS. (2006).
Bequé, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: an empirical evaluation.
Bleich, J., Kaperner, A., Geroge, E. I., & Jensen, S. T. (2014). Variable selection for BART: an application to gene regulation.
Breiman, L. (1996). Bagging predictors.
Breiman, L. (2001). Random forests.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984).
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets.
Carpenter, J., & Bithell, J. (2000). Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.
Chandler, G. G., & Coffman, J. Y. (1979). A comparative analysis of empirical vs. judgmental credit evaluation.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique.
Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART model search.
Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian Additive and Regression Trees.
Crook, J., & Bellotti, T. (2010). Time varying and dynamic models for default risk in consumer loans.
Delong, E. R., Delong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
Desai, V. S., Crook, J. N., & Overstreet, G. A. (1996). A comparison of neural networks and linear scoring models in the credit union environment.
Durand, D. (1941).
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression.
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine.
Für, F., Lima, J. D., & Schenatto, F. J. A. (2017). Uma revisão sistemática da literatura sobre credit scoring. In:
Gestel, T. V., Baesens, B., Suykens, J. A. K., Poel, D. V., Baestaens, D. E., & Willekens, M. (2006). Bayesian kernel based classification for financial distress detection.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013).
Hsieh, N.-C. (2005). Hybrid mining approach in the design of credit scoring models.
Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: a review.
Kapelner, A., & Bleich, J. (2013).
King, G., & Zeng, L. (2001). Logistic regression in rare events data.
Kraus, A. (2014).
Kruppa, J., Schwarzb, A., Armingerb, G., & Ziegler, A. (2013). Consumer credit risk: Individual probability estimates using machine learning.
Lee, T.-S., & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines.
Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming.
Leong, C. K. (2016). Credit risck scoring with Bayesian network models.
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research.
Li, S.-T., Shiue, W., & Huang, M.-H. (2006). The evaluation of consumer loans using support vector machines.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest.
Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: systematic review and overall comparison.
Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via random forest.
Malhotra, R., & Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems.
Pavlidis, N. G., Tasoulis, D. K., Adams, N. M., & Hand, D. J. (2012). Adaptive consumer credit classification.
R Core Team. (2016).
Siddiqi, N. (2012).
Sousa, M. R., Gama, J., & Brandão, E. (2016). A new dynamic modeling framework for credit risk assessment.
Thomas, L. C. (2009).
Thomas, L. C., Oliver, R. W., & Hand, D. J. (2005). A survey of the issues in consumer credit modelling research.
Wei, G., Yun-Zhong, C., & Minh-Shu, C. (2014). A new dynamic credit scoring model based on the objective cluster analysis. In Z. Wen, & T. Li (Ed.),
West, D., Dellana, S., & Qian, J. (2005). Neural network ensemble strategies for financial decision applications.
Xia, Y., Liu, B., Wang, S., & Lai, K. K. (2000). A model for portfolio selection with order of expected returns.
Yap, B. W., Rani, K. A., Rahman, H. A. A., Fong, S., Khairudin, Z., & Abdullah, N. N. (2014). An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. In T. Herawan, M. Deris, & J. Abawajy (Ed.),
Yeh, C.-C., Lin, F., & Hsu, C.-Y. (2012). A hybrid KVM model, random forests and rough set theory approach for credit rating.
Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004). Small business credit scoring: a comparison of logistic regression, neural network, and decision tree models. In
Zhang, J. L., & Härdle, W. K. (2010). The Bayesian Additive Classification Tree applied to credit risk modelling.
Zhou, L., & Wang, H. (2012). Loan default prediction on large imbalanced data using random forest.