• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.85-94
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• 2013

금융시장의 규모가 점점 더 커짐에 따라 고객정보 관리 미숙 또는 부실한 의사결정, 즉 신용 리스크 관리 실패로 인한 손실이 막대하게 증가하고 있다. 따라서 신용 리스크 관리가 점차 더 중요해지고, 이런 신용 리스크를 최소화하는 기본적인 도구인 신용 평점 모형이 절실히 요구된다. 신용평점 모형은 주로 이항형 목표변수만 이용하여 개발 연구되었다. 본 논문에서는 순서형 다항 자료 또는 경시적 이항 자료 같은 다른 형태의 목표 변수를 고려한 신용평점 모형구축 방법을 제시한다. 그 개발된 모형을 실제 자료와 랜덤화한 자료에 적용하여 Kolmogorov-Smirnov 통계량으로 비교 분석한다.