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Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Kuosmanen, Timo, Professor, Aalto University, Finland
dc.contributor.author Dai, Xiaofeng
dc.date.accessioned 2016-08-27T09:01:17Z
dc.date.available 2016-08-27T09:01:17Z
dc.date.issued 2016
dc.identifier.isbn 978-952-60-6907-4 (electronic)
dc.identifier.isbn 978-952-60-6906-7 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/21682
dc.description.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. en
dc.format.extent 89
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 134/2016
dc.relation.haspart Xiaofeng Dai. Non-parametric efficiency estimation using Richardson-Lucy blind deconvolution. European Journal of Operational Research, 248, 731-739, 2016.
dc.relation.haspart 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.
dc.relation.haspart Xiaofeng Dai, Timo Kuosmanen. Best-practice benchmarking using clustering methods: Application to energy regulation. Omega, 42 (1), 179-188, 2014.
dc.relation.haspart 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.
dc.subject.other Computer science en
dc.subject.other Consumption, Services en
dc.subject.other Economics en
dc.title Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Kauppakorkeakoulu fi
dc.contributor.school School of Business en
dc.contributor.department Tieto- ja palvelutalouden laitos fi
dc.contributor.department Department of Information and Service Economy en
dc.subject.keyword Productive efficiency analysis en
dc.subject.keyword Benchmarking en
dc.subject.keyword Deconvolution en
dc.subject.keyword Clustering en
dc.subject.keyword StoNED en
dc.identifier.urn URN:ISBN:978-952-60-6907-4
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.opn Johnson, Andrew, Professor, Texas A&M University, USA
dc.contributor.lab Management Science en
dc.contributor.lab Johtaminen fi
dc.date.defence 2016-08-26

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