We investigate the individual donor behavior using both classical statistical techniques and a mathematical programming formulation. The study entails classifying individual donors based on their donation levels, a response variable. We use individuals’ income levels, savings, and age as predictor variables. For this study, we use the characteristics of a real dataset to simulate multiple datasets of donors and their characteristics. The results of a simulation experiment show that the weighted linear programming model consistently outperforms standard statistical approaches in attaining lower APparent Error Rates (APERs) for 100 replications in each of the three correlation cases.
Lawrence, K.D., Pai, D.R., Klimberg, R. and Lawrence, S.M. (2008), "Understanding donor behavior: An empirical study of statistical and non-parametric methods", Lawrence, K.D. and Geurts, M.D. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 5), Emerald Group Publishing Limited, Bingley, pp. 281-291. https://doi.org/10.1016/S1477-4070(07)00216-4
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