Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach
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Journal Title
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School of Business |
Doctoral thesis (article-based)
| Defence date: 2016-08-26
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Author
Date
2016
Major/Subject
Mcode
Degree programme
Language
en
Pages
89
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 134/2016
Abstract
This dissertation explores the interdisciplinary applications of computational methods in quantitative economics. Particularly, this thesis focuses on problems in productive efficiency analysis and benchmarking that are hardly approachable or solvable using conventional methods. In productive efficiency analysis, null or zero values are often produced due to the wrong skewness or low kurtosis of the inefficiency distribution as against the distributional assumption on the inefficiency term. This thesis uses the deconvolution technique, which is traditionally used in image processing for noise removal, to develop a fully non-parametric method for efficiency estimation. Publications 1 and 2 are devoted to this topic, with focus being laid on the cross-sectional case and panel case, respectively. Through Monte-Carlo simulations and empirical applications to Finnish electricity distribution network data and Finnish banking data, the results show that the Richardson-Lucy blind deconvolution method is insensitive to the distributio-nal assumptions, robust to the data noise levels and heteroscedasticity on efficiency estimation. In benchmarking, which could be the next step of productive efficiency analysis, the 'best practice' target may not perform under the same operational environment with the DMU under study. This would render the benchmarks impractical to follow and adversely affects the managers to make the correct decisions on performance improvement of a DMU. This dissertation proposes a clustering-based benchmarking framework in Publication 3. The empirical study on Finnish electricity distribution network reveals that the proposed framework novels not only in its consideration on the differences of the operational environment among DMUs, but also its extreme flexibility. We conducted a comparison analysis on the different combinations of the clustering and efficiency estimation techniques using computational simulations and empirical applications to Finnish electricity distribution network data, based on which Publication 4 specifies an efficient combination for benchmarking in energy regulation. This dissertation endeavors to solve problems in quantitative economics using interdisciplinary approaches. The methods developed benefit this field and the way how we approach the problems open a new perspective.Description
Thesis advisor
Kuosmanen, Timo, Professor, Aalto University, FinlandKeywords
Productive efficiency analysis, Benchmarking, Deconvolution, Clustering, StoNED
Other note
Parts
- Xiaofeng Dai. Non-parametric efficiency estimation using Richardson-Lucy blind deconvolution. European Journal of Operational Research, 248, 731-739, 2016.
- Xiaofeng Dai. Non-parametric efficiency estimation in a panel setting: corrected Richardson-Lucy blind deconvolution. Proceedings of 2015 International Conference on Management Engineering and Information Technology Application, Hong Kong, China, April 19-20, 2015.
- Xiaofeng Dai, Timo Kuosmanen. Best-practice benchmarking using clustering methods: Application to energy regulation. Omega, 42 (1), 179-188, 2014.
- Xiaofeng Dai. NMM-StoNED: a normal mixture model based stochastic semi-parametric bench-marking method. International Journal of Business and Management Study, 2 (2), 2015.
