A Robust Credit Screening Model Using Categorical Data
从决策理论角度重新审视信用筛选问题，比较了数学规划与线性判别分析等方法，提出适用于二元数据的稳健筛选规则，并在公共事业中成功应用。
Motivated by an application in a public utility, the credit screening problem is re-examined from a decision theoretic viewpoint. The relationships between several alternative problem formulations are explored, and compared to the classical linear discriminant analysis (LDA) approach. Several mathematical programming based solution methods are proposed when the data are binary, and an efficient algorithm is developed for the case when the screening function must also have binary weights. Actual results of both the mathematical programming and LDA methods are presented and compared. The resulting mathematical programming rules are effective, robust, and flexible to administer. Practical advantages of the resulting “n out of N” type rules are discussed. These screening rules have been widely implemented by a major public utility and have resulted in substantial benefits to the utility and to the public.