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Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data

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  • Zelenyuk, Valentin

Abstract

The main goal of this paper is to explore the possible solutions to a ‘big data’ problem related to the very large dimensions of input–output data. In particular, we focus on the cases of severe ‘curse of dimensionality’ problem that require dimension-reduction prior to using Data Envelopment Analysis. To achieve this goal, we have presented some theoretical grounds and performed a new to the literature simulation study where we explored the price-based aggregation as a solution to address the problem of very large dimensions.

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  • Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:1:p:172-187
    DOI: 10.1016/j.ejor.2019.08.007
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    Cited by:

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    3. Bao Hoang Nguyen & Valentin Zelenyuk, 2020. "Robust efficiency analysis of public hospitals in Queensland, Australia," CEPA Working Papers Series WP052020, School of Economics, University of Queensland, Australia.
    4. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.
    5. Simar, Léopold & Zelenyuk, Valentin, 2020. "Improving finite sample approximation by central limit theorems for estimates from Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1002-1015.
    6. Natalya Zelenyuk & Valentin Zelenyuk, 2021. "Bank Performance Analysis," CEPA Working Papers Series WP022021, School of Economics, University of Queensland, Australia.
    7. Bao Hoang Nguyen & Valentin Zelenyuk, 2021. "Aggregate efficiency of industry and its groups: the case of Queensland public hospitals," Empirical Economics, Springer, vol. 60(6), pages 2795-2836, June.
    8. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP022020, School of Economics, University of Queensland, Australia.
    9. Ali Homayoni & Reza Fallahnejad & Farhad Hosseinzadeh Lotfi, 2022. "Cross Malmquist Productivity Index in Data Envelopment Analysis," 4OR, Springer, vol. 20(4), pages 567-602, December.
    10. S. C. West & A. W. Mugera & R. S. Kingwell, 2022. "The choice of efficiency benchmarking metric in evaluating firm productivity and viability," Journal of Productivity Analysis, Springer, vol. 57(2), pages 193-211, April.
    11. Emil Heesche & Mette Asmild, 2022. "Implications of Aggregation Uncertainty in DEA," IFRO Working Paper 2022/02, University of Copenhagen, Department of Food and Resource Economics.
    12. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    13. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    14. Valentin Zelenyuk, 2021. "Performance Analysis: Economic Foundations & Trends," CEPA Working Papers Series WP162021, School of Economics, University of Queensland, Australia.
    15. Kok Fong See & Shawna Grosskopf & Vivian Valdmanis & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP072021, School of Economics, University of Queensland, Australia.
    16. Valentin Zelenyuk, 2021. "Performance Analysis: Economic Foundations and Trends," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(3), pages 153-229, September.
    17. Chen Chunhua & Liu Haohua & Tang Lijun & Ren Jianwei, 2021. "A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs," Journal of Systems Science and Information, De Gruyter, vol. 9(4), pages 378-398, August.
    18. Rolf Färe & Valentin Zelenyuk, 2020. "Profit Efficiency and its Estimation," CEPA Working Papers Series WP072020, School of Economics, University of Queensland, Australia.
    19. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).

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