Evaluation of empirical attributes for credit risk forecasting from numerical data
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S. E. Papadakis | Alexandros Garefalakis | Augustinos Dimitras | S. Papadakis | Alexandros Garefalakis | A. Dimitras
[1] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[2] Fengxi Song,et al. Feature Selection Using Principal Component Analysis , 2010, 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization.
[3] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[4] C. Zopounidis,et al. Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment , 2001 .
[5] R. Gonzalez. Applied Multivariate Statistics for the Social Sciences , 2003 .
[6] Alexander J. Smola,et al. Learning with kernels , 1998 .
[7] L. Shenton,et al. Omnibus test contours for departures from normality based on √b1 and b2 , 1975 .
[8] Alexandros Garefalakis,et al. HOW NARRATIVE REPORTING CHANGED THE BUSINESS WORLD: PROVIDING A NEW MEASUREMENT TOOL , 2016 .
[9] Kenneth N. Daniels,et al. Information, Credit Risk, Lender Specialization and Loan Pricing: Evidence from the DIP Financing Market , 2008 .
[10] Mu-Chen Chen,et al. Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..
[11] J. Hołyst,et al. Collective firm bankruptcies and phase transition in rating dynamics , 2009, 0904.4430.
[12] Fotios Pasiouras,et al. The Prediction of Bank Acquisition Targets with Discriminant and Logit Analyses: Methodological Issues and Empirical Evidence , 2009 .
[13] S. Kung,et al. Neural networks for extracting unsymmetric principal components , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.
[14] João Fernandes,et al. Corporate Credit Risk Modeling: Quantitative Rating System and Probability of Default Estimation , 2005 .
[15] J. Sola,et al. Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .
[16] Juha Karhunen,et al. Principal component neural networks — Theory and applications , 1998, Pattern Analysis and Applications.
[17] Linda Reichwein Zientek,et al. Book Review: Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications , 2007 .
[18] R. D'Agostino. An omnibus test of normality for moderate and large size samples , 1971 .
[19] Gregory W. Corder,et al. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach , 2009 .
[20] Lloyd A. Smith,et al. Practical feature subset selection for machine learning , 1998 .
[21] Bernhard Schölkopf,et al. Learning with kernels , 2001 .
[22] Kin Keung Lai,et al. Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection , 2011, Expert Syst. Appl..
[23] K. Kosmidou,et al. Domestic and multinational determinants of foreign bank profits: The case of Greek banks operating abroad , 2007 .
[24] Paul R. Yarnold,et al. Reading and Understanding Multivariate Statistics , 1995 .
[25] Vladimir Vapnik,et al. Principles of Risk Minimization for Learning Theory , 1991, NIPS.
[26] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[27] Constantin Zopounidis,et al. The impact of bank regulations, supervision, market structure, and bank characteristics on individual bank ratings: A cross-country analysis , 2006 .
[28] Alexandros Benos,et al. Extending the Merton Model: A hybrid approach to assessing credit quality , 2007, Math. Comput. Model..
[29] A. Duff,et al. Credit ratings quality: The perceptions of market participants and other interested parties , 2009 .