A New Framework of Combinational Evaluation Methodology Based on Statistics

Zhongping Wang, Weidong Li
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Abstract

Comparing with single evaluation method, combinational evaluation method is more informative, more objective and more accurate. A new framework of combinational evaluation methodology based on statistics is constructed in this paper. At first the Kendall coefficient of consistency is computed to identify whether the different single evaluation methods are compatible. A nonlinear programming model is constructed to determine the weight of different evaluation method result. The effect of the combinational method is determined by the index of MAPE and MSPE. Then 20 listed companies in logistics industry are chosen as sample. Then the evaluation index system is constructed. Based on 4 single evaluation methods which are synthetic index, TOPSIS, efficiency coefficient method and factor analysis, combinational evaluation is constructed with the nonlinear programming model. The exploratory results show that combinational evaluation method is a good choice.
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基于统计学的组合评价方法新框架
与单一评价方法相比,组合评价方法信息量更大、更客观、更准确。本文构建了一种新的基于统计学的组合评价方法框架。首先计算肯德尔一致性系数,以确定不同的单一评价方法是否相容。建立非线性规划模型,确定不同评价方法结果的权重。结合MAPE和MSPE指标确定了组合方法的效果。然后选取20家物流业上市公司作为样本。然后构建了评价指标体系。在综合指数法、TOPSIS法、效率系数法和因子分析法4种单一评价方法的基础上,利用非线性规划模型构建了组合评价方法。探索性结果表明,组合评价方法是一种较好的选择。
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