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This paper uncovers the-mysterious veil-above the formulations and concerned properties of existing weighted additive data envelopment analysis WADD models associated with dataset standardization techniques. Based on the truth that the formulation of objective functions in WADD models seems random and confused for users, the study investigates the correspondence relationship between the formulation of objective functions by statistical data-based weights aggregating slacks in WADD models and the pre-standardization of original datasets before using the traditional ADD model in terms of satisfying unit and translation invariance. Our work presents a statistical background for WADD models’ formulations, and makes them become more interpretive and more convenient to be computed and practically applied. Based on the pre-standardization techniques, two new WADD models satisfying unit invariance are formulated to enrich the family of WADD models. We compare all WADD models in some concerned properties, and give special attention to the inefficiency discrimination power of them. Moreover, some suggestions guiding theoretical and practical applications of WADD models are discussed.

Item Type: MPRA Paper -

Original Title: Weighted Additive DEA Models Associated with Dataset Standardization Techniques-

Language: English-

Keywords: Data envelopment analysis; Weighted additive models; Formulations and applications; Dataset standardization techniques-

Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and EstimationC - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic AnalysisO - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives-

Author: Chen, Kaihua

Source: https://mpra.ub.uni-muenchen.de/55072/

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