1 |
Agresti, A. (2002). Categorical data analysis, 2nd Ed., Wiley-Interscience, New York.
|
2 |
Bielecki, T. R. and Rutkowski, M. (2002). Credit risk: Modeling, valuation and hedging, Springer-Verlag, Berlin.
|
3 |
Gonsalves, M. H. and Azzalini, A. (2008). Using markov chains for marginal modelling of binary longitudinal data in an exact likelihood approach. Metron, 2, 157-181.
|
4 |
Gonsalves, M. H., Cabral, M. S. and Azzalini, A. (2012). The R package bild for the analysis of binary longitudinal data. Journal of Statistical Software, 9, 1-17.
|
5 |
Hand, D. J. and Adams, N. M. (2000). Defining attributes for scorecard construction in credit scoring. Journal of Applied Statistics, 27, 527-540.
DOI
ScienceOn
|
6 |
Jung, K. M. (2010). Development of educational software for coarse classifying and model evaluation in credit scoring. Journal of the Korean Data & Information Science Study, 21, 1225-1235.
|
7 |
Kang, H. C., Han, S. T., Choi, J. H., Lee, S. G., Kim, E. S., Um, I. H. and Kim, M. K. (2006), Methodology of data mining for C.R.M. : A case study on Enterprise Miner, Free Academy, Seoul.
|
8 |
Kim, E. N. and Ha, J. (2010). Study on the validation methods of calibration considering correlations. Journal of the Korean Data & Information Science Study, 21, 407-417.
|
9 |
Kim, M. J. (2004). Understanding and applying credit scores, ePharos, Seoul.
|
10 |
Koo, J., Park, C. and Jhun, M. (2009). A classification spline machine for building a credit scorecard. Journal of Statistical Computation and Simulation, 79, 681-689.
DOI
ScienceOn
|
11 |
Thomas, L. C., Edelman, D. B. and Crook, J. L. (2002). Credit scoring and its applications, SIAM, Philadelphia.
|