References
- H.A. Abdou and J. Pointon, Credit scoring, statistical techniques and evaluation criteria: a review of the literature, Intell. Syst. Account., Finance Manage. 18 (2011), pp. 59–88.
- A. Agresti, Categorical Data Analysis, Vol. 482, John Wiley & Sons, Hoboken, NJ, 2003.
- A. Agresti, Foundations of Linear and Generalized Linear Models, John Wiley and Sons, Hoboken, NJ, 2015.
- P.D. Allison, Logistic Regression Using SAS: Theory and Application, SAS Institute, NC, 2012.
- E. Arjas and P. Haara, A logistic regression model for hazard: asymptotic results, Scand. J. Stat. 242(2) (1987), pp. 546–556.
- P. Barrieu and G. Scandolo, Assessing financial model risk, Eur. J. Oper. Res. 242 (2015), pp. 546–556.
- J. Begley, J. Ming, and S. Watts, Bankruptcy classification errors in the 1980s: an empirical analysis of altman's and ohlson's models, Rev. Account. Studies 1 (1996), pp. 267–284.
- D. Berg, Bankruptcy prediction by generalized additive models, Appl. Stoch. Models. Bus. Ind. 23 (2007), pp. 129–143.
- R. Calabrese, Predicting bank loan recovery rates with a mixed continuous-discrete model, Stochastic Models in Business and Industry¡/DIFdel¿Appl. Stoch. Models. Bus. Ind. 30 (2014), pp. 99–114.
- R. Calabrese and P. Giudici, Estimating bank default with generalised extreme value regression models, J. Oper. Res. Soc. 66 (2015), pp. 1783–1792.
- R. Calabrese and S.A. Osmetti, Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model, J. Appl. Stat. 40 (2013), pp. 1172–1188.
- R. Calabrese and S.A. Osmetti, Generalized extreme value regression for binary rare events data: an application to credit defaults, Bull. Int. Stat. Inst. LXII, 58th Session Int. Stat. Inst. (2011), pp. 5631–5634.
- N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, Smote: synthetic minority over-sampling technique, J. Artif. Intell. Res. 16 (2002), pp. 321–357.
- D.R. Cox, Analysis of Binary Data, Routledge, New York, 2018.
- E. Demidenko, Computational aspects of probit model, Math. Commun. 6 (2001), pp. 233–247.
- E. Derman, Model Risk, Quantitative Strategies Research Notes, Vol. 7, Goldman Sachs, New York, NY, 1996.
- V.S. Desai, D.G. Conway, J.N. Crook, and G.A. Overstreet Jr, Credit-scoring models in the credit-union environment using neural networks and genetic algorithms, IMA J. Manage. Math. 8 (1997), pp. 323–346.
- J. Friedman, T. Hastie, and R. Tibshirani, The Elements of Statistical Learning (Vol. 1, No. 10), Springer series in statistics, New York, 2001.
- D.C. Heilbron, Zero-altered and other regression models for count data with added zeros, Biom. J. 36 (1994), pp. 531–547.
- S.R. Islam, W. Eberle, and S.K. Ghafoor, Credit default mining using combined machine learning and heuristic approach, arXiv preprint arXiv:1807.01176 (2018)
- W.H. Jefferys and J.O. Berger, Sharpening Ockham's razor on a Bayesian strop, Technical Report (1991).
- J. Kerkhof, B. Melenberg, and H. Schumacher, Model risk and capital reserves, J. Bank. Financ. 34 (2010), pp. 267–279.
- A.E. Kitali, E. Kidando, T. Sando, R. Moses, and E.E. Ozguven, Evaluating aging pedestrian crash severity with Bayesian complementary log–log model for improved prediction accuracy, Transp. Res. Rec. 2659 (2017), pp. 155–163. https://doi.org/https://doi.org/10.3141/2659-17.
- L.C. Lin, P.H. Huang, and L.J. Weng, Selecting path models in sem: A comparison of model selection criteria, Struct. Eq. Model.: A Multidiscipl. J. 24 (2017), pp. 855–869.
- F. Lindsten, N. Wahlström, A. Svensson, and T.B. Schön, Statistical Machine Learning, Uppsala University: Lecture note -- Department of Information Technology, 2018
- J.L. Martin and D. Wu, Pedestrian fatality and impact speed squared: cloglog modeling from french national data, Injury Prevention¡/DIFdel¿Traffic. Inj. Prev. 19 (2018), pp. 94–101.
- H.P. Mashele, Aligning the economic capital of model risk with the strategic objectives of an enterprise, Potchefstroom: North-West University, Master of Business Administration-dissertation, 2016
- A.J. McNeil, R. Frey, and P. Embrechts, Quantitative Risk Management: Concepts, Techniques and Tools, Vol. 3, Princeton university press Princeton, NJ, 2005.
- A.J. McNeil and J.P. Wendin, Bayesian inference for generalized linear mixed models of portfolio credit risk, J. Empirical Finance 14 (2007), pp. 131–149.
- D. Memić, Assessing credit default using logistic regression and multiple discriminant analysis: empirical evidence from Bosnia and Herzegovina, Interdiscipl. Descrip Complex Syst. INDECS 13 (2015), pp. 128–153.
- T.P. Minka, A comparison of numerical optimizers for logistic regression, Unpublished Draft (2003), pp. 1–18.
- J. Mullahy, Specification and testing of some modified count data models, Econometrics¡/DIFdel¿J. Econom. 33 (1986), pp. 341–365.
- M. Müller, Generalized linear models, Handbook of Computational Statistics, Springer, 2012, pp. 681–709
- J.A. Nelder and R.W. Wedderburn, Generalized linear models, J. R. Stat. Soc.: Seri. A (Gen.) 135 (1972), pp. 370–384.
- J. Neter, M.H. Kutner, C.J. Nachtsheim, and W. Wasserman, Applied Linear Statistical Models, Vol. 4, Irwin Chicago, Cambridge, 1996. Available at http://research.microsoft.com/∼minka/papers/logreg/minka-logreg.
- OCC, Model risk, quantitative strategies research notes, Washington, Federal Reserve Bank -- Comptroller of the Currency's, 2011.
- J.A. Ohlson, Financial ratios and the probabilistic prediction of bankruptcy, J. Account. Res. 18(1) (1980), pp. 109–131.
- A.D. Penman and W.D. Johnson, Complementary log–log regression for the estimation of covariate-adjusted prevalence ratios in the analysis of data from cross-sectional studies, Biom. J.: J. Math. Methods Biosci. 51 (2009), pp. 433–442.
- M. Pohar, M. Blas, and S. Turk, Comparison of logistic regression and linear discriminant analysis: a simulation study, Metodoloski Zvezki 1 (2004), pp. 143.
- S. Yang and H. Zhang et al., Comparison of several data mining methods in credit card default prediction, Intell. Inform. Manage. 10 (2018), pp. 115.
- I.C. Yeh and C.h. Lien, The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert. Syst. Appl. 36 (2009), pp. 2473–2480.