• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.1013-1026
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• 2008

Kolmogorov-Smirnov(K-S) statistic has been widely used for the model validation of credit rating models. Validation criteria for the K-S statistic is empirically used at the levels of 0.3 or 0.4 which are much larger than the critical values of K-S test statistic. We examine whether these criteria are reasonable and appropriate through the simulations according to various sample sizes, type II error rates, and the ratio of bads among data. The simulation results say that the currently used validation criteria are too lower than values of K-S statistics obtained from any credit rating models in Korea, so that any credit rating models have good discriminatory power. In this work, alternative criteria of K-S statistic are proposed as critical levels under realistic situations of credit rating models.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.889-899
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• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.239-347
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• 2009

This paper presents Kolmogorov-Smirnov, mean difference, AUROC and AR, four well known statistics that have been widely used for evaluating the discriminatory power of credit rating models. Criteria for these statistics are determined by the value of mean difference under the assumption of normality and equal standard deviation. Alternative criteria are proposed through the simulations according to various sample sizes, type II error rates, and the ratio of bads, also we suggest the meaning of statistic on the basis of discriminatory power. Finally we make a comparative study of the currently used guidelines and simulated results.