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The Use of Yanai’s Generalized Coefficient of Determination to Reduce the Number of Variables in DEA Models

The Use of Yanai’s Generalized Coefficient of Determination to Reduce the Number of Variables in DEA Models
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摘要 This paper proposes a new method to reduce the dimensionality of input and output spaces in DEA models. The method is based on Yanai’s Generalized Coefficient of Determination and on the concept of pseudo-rank of a matrix. In addition, the paper suggests a rule to determine the cardinality of the subset of selected variables in a way to gain the maximal discretionary power and to suffer a minimal informational loss. This paper proposes a new method to reduce the dimensionality of input and output spaces in DEA models. The method is based on Yanai’s Generalized Coefficient of Determination and on the concept of pseudo-rank of a matrix. In addition, the paper suggests a rule to determine the cardinality of the subset of selected variables in a way to gain the maximal discretionary power and to suffer a minimal informational loss.
机构地区 School of Business
出处 《American Journal of Operations Research》 2017年第3期187-200,共14页 美国运筹学期刊(英文)
关键词 DEA Yanai’s GENERALIZED Coefficient of Determination Pseudo-Rank DIMENSION Reduction Improving DISCRIMINATION DEA Yanai’s Generalized Coefficient of Determination Pseudo-Rank Dimension Reduction Improving Discrimination
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