Modified Test Statistic For Identity Of Two Distribution On Credit Evaluation

KOREAN JOURNAL OF APPLIED STATISTICS(2009)

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摘要
The probability of default on the credit evaluation study is represented as a linear combination of two distributions of default and non-default, and the distribution of the probability of default are generally known in most cases. Except the well-known Kolmogorov-Smirnov statistic for testing the identity of two distribution, Kuiper, Cramer-Von Mises, Anderson-Darling, and Watson test statistics are introduced in this work. Under the assumption that the population distribution is known, modified Cramer-Von Mises, Anderson-Darling, and Watson statistics are proposed. Based on score data generated from various probability density functions of the probability of default, the modified test statistics are discussed and compared.
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关键词
Credit rating model, score, discriminatory power, distribution function, nonparametric test, probability of default, skewness, validation
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