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Macroeconomic Factors in Modelling the SMEs Bankruptcy Risk. The Case of the Polish Market

Bibliography Agrawal K., Maheshwari Y., 2014, Default risk modelling using macroeconomic variables , Journal of Indian Business Research, vol. 6. Altman E.I., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy , The Journal of Finance, vol. 23, no. 4, pp. 589-609. Bellotti A., Crook J., 2009, Credit scoring with macroeconomic variables using survival analysis , Journal of the Operational Research Society, no. 60(12), pp. 1699-1707. Bellovary J.L., Giacomino D.E., Akers M.D., 2007, A review of

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Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines

Bibliography Abildgren K., Buchholst B.V., Staghøj J., 2011, Bank-firm relationships and the performance of non-financial firms during the financial crisis 2008-09 microeconometric evidence from large-scale firm-level data , Working Paper, no. 73. Danmarks National Bank. Akbani R., Kwek S., Japkowicz N., 2004, Applying support vector machines to imbalanced datasets , In Machine Learning: ECML 2004. Springer, pp. 39-50. Anderson R., 1999, The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation

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Item Response Theory Models in the Measurement Theory with the Use of ltm Package in R

References Andrich D., 1978, Application of a psychometric rating model to ordered categories which are scored with successive integers, Applied Psychological Measurement, 2, pp. 581-594. Bates D.M., 2008, Fitting mixed-effects models using the lme4 package in R, Barton M.A., Lord F.M., 1981, An upper asymptote for the three-parameter logistic item-response model, Princeton, NJ: Educational Testing Service. Birnbaum A., 1957, Efficient design

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Latent Class Analysis in the Evaluation of Items in Survey Research

, Psychological Bulletin, 45, pp. 507-530. Lord F.M., 1952, The relation of the reliability of multiple-choice tests to the distribution of item difficulties, Psychometrika, 17(2), pp. 181-193. Lord F.M., 1953, The relation of test score to the trait underlying the test, Educational and Psychological Measurement, 13, pp. 517-548. McCutcheon A.L., 1987, Latent Class Analysis, Sage University Paper. Sage Publications, Newbury Park. Raftery A.E., 1986, Choosing models for cross-classification, Amer. Sociol. Rev., 51

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The Impact of Decision-Making Profiles on the Consistency of Rankings Obtained by Means of Selected Multiple Criteria Decision-Aiding Methods

, SAW-Based Rankings vs. Intrinsic Evaluations of the Negotiation Offers – an Experimental Study, [in:] Lecture Notes in Business Information Processing. Group Decision and Negotiation. A Process-Oriented View , eds P. Zaraté, G.E. Kersten, J.E. Hernández, Springer, pp. 176-183. Roszkowska E., Wachowicz T., 2016, Analyzing the applicability of selected MCDA methods for determining the reliable scoring systems , Proceedings of the 16th International Conference on Group Decision and Negotiation Bellingham, D.S. Bajwa, S. Koeszegi, R. Vetschera (eds), Western

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Competing Risk Models of Default in the Presence of Early Repayments

Statistics in Quality and Reliability , Wiley, Chichester. Narain B., 1992, Survival Analysis and the Credit-Granting Decision, [in:] Thomas L., Crook J., Edelman D. (ed.), Credit Scoring and Credit Control , Oxford University Press, pp. 109-122. Ng S.K., McLachlan G.J., 2003, An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data , Statistics in Medicine, 22, pp. 1097-1111. Nicolaie M.A., van Houwelingen H.C., Putter H., 2010, Vertical modeling: a pattern mixture approach for competing risks modeling

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