Chen, Kaihua (2014): Weighted Additive DEA Models Associated with Dataset Standardization Techniques.
Preview |
PDF
MPRA_paper_55072.pdf Download (354kB) | Preview |
Abstract
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 (in)efficiency 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 Estimation C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives |
Item ID: | 55072 |
Depositing User: | Professor Kaihua Chen |
Date Deposited: | 06 Apr 2014 16:28 |
Last Modified: | 27 Sep 2019 16:52 |
References: | Aida K, Cooper WW, Pastor JT, Sueyoshi T (1998). Evaluating Water Supply Services in Japan with RAM–—A Range-Adjusted Measure of Inefficiency. Omega 26:207-232. Ali AI, Lerme CS, Seiford LM (1995). Components of efficiency evaluation in data envelopment analysis. European Journal of Operational Research 80: 462-473. Ali AI, Seiford, LM (1990). Translation invariance in data envelopment analysis. Operations Research Letters 9:403-405. Avkiran NK (2009). Removing the impact of environment with units-invariant efficient frontier analysis: An illustrative case study with intertemporal panel data. Omega, 37:535-544. Banker RD (1984) Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research 17:35-44. Bardhan I, Bowlin WF, Cooper WW, Sueyoshi T (1996). Models and Measures for Efficiency Dominance in DEA. Part I: Additive Models and MED Measures. Journal of the Operations Research Society of Japan 39:322-332. Charnes A, Cooper WW, Golany B, Seiford L, Stutz J (1985). Foundations of Data Envelopment Analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics 30:97-107. Charnes A, Cooper WW, Rhodes E (1978). Measuring the efficiency of decision making units. European Journal of Operational Research 2:429-444. Cook WD, Seiford LM (2009). Data envelopment analysis (DEA) – Thirty years on. European Journal of Operational Research 192:1-17. Cooper WW, Park KS, Pastor JT (1999). RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA. Journal of Productivity Analysis 11:5-42. Cooper WW, Pastor JT, Aparicio J, Pastor D (2011).BAM: a bounded adjusted measure of efficiency for use with bounded additive models 2:85-94. Cooper WW, Seiford LM, Tone K, Zhu J (2007). Some models and measures for evaluating performances with DEA: past accomplishments and future prospects. Journal of Productivity Analysis 28:151-163. Fried HO, Lovell CAK, Schmidt SS, Yaisawarng S (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 17:157-174. Fried HO, Schmidt, SS, Yaisawarng S (1999). Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis 12:249-267. Glass JC, McCallion G, McKillop DG, Stringer K (2006). A ‘technically level playing-field’ profit efficiency analysis of enforced competition between publicly funded institutions. European Economic Review 50:1601-1626. Liu J, Tone K (2008). A multistage method to measure efficiency and its application to Japanese banking industry. Socio-Economic Planning Sciences 42:75-91. Lovell CAK, Pastor JT (1995). Units invariant and translation invariant DEA models. Operations Research Letters 18:147-151. Margari BB, Erbetta F, Carmelo P, Piacenza M (2007). Regulatory and environmental effects on public transit efficiency: a mixed DEA-SFA approach. Journal of Regulatory Economics 32:131-151. Pastor JT (1994). New Additive Models for Handling Zero and Negative Data. Working Paper, Departamento de Estadistica e Investigacion Operativa, Universidad de Alicante, Spain. Pastor JT (1996). Translation invariance in data envelopment analysis: A generalization. Annals of Operations Research 66:93-102. Pastor JT, Ruiz J (2007). Variables With Negative Values In Dea. In: Zhu J, Cook WD. (Eds.) Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. Springer, New York. Simonoff JS (1996). Smoothing Methods in Statistics. Springer, New York. Sueyoshi T, Sekitani K (2009). An occurrence of multiple projections in DEA-based measurement of technical efficiency: Theoretical comparison among DEA models from desirable properties. European Journal of Operational Research 196: 764-794. Thrall RM (1996). Duality, classification and slacks in DEA. Annals of Operations Research 66: 109-138. Tone K (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130:498-509. Wand MP, Jones MC (1995). Kernel Smoothing. Chapman and Hall, London. Wang EC, Huang W (2007). Relative efficiency of R&D activities: An across-country study accounting for environmental factors in the DEA approach. Research Policy 36:260-273. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/55072 |