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ROC Curve Fitting with Normal Mixtures

정규혼합분포를 이용한 ROC 분석

Hong, Chong-Sun;Lee, Won-Yong
홍종선;이원용

  • Received : 20101200
  • Accepted : 20110200
  • Published : 2011.04.30

Abstract

There are many researches that have considered the distribution functions and appropriate covariates corresponding to the scores in order to improve the accuracy of a diagnostic test, including the ROC curve that is represented with the relations of the sensitivity and the specificity. The ROC analysis was used by the regression model including some covariates under the assumptions that its distribution function is known or estimable. In this work, we consider a general situation that both the distribution function and the elects of covariates are unknown. For the ROC analysis, the mixtures of normal distributions are used to estimate the distribution function fitted to the credit evaluation data that is consisted of the score random variable and two sub-populations of parameters. The AUC measure is explored to compare with the nonparametric and empirical ROC curve. We conclude that the method using normal mixtures is fitted to the classical one better than other methods.

Keywords

Classification model;credit evaluation;quasi-likelihood;threshold

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