Best-practice benchmarking using clustering methods: Application to energy regulation

Omega (United Kingdom)

Volumn 42, Issue 1, 2014, Pages 179-188

  Dai, Xiaofeng ^a   Kuosmanen, Timo ^a  

^a HELSINKI SCHOOL OF ECONOMICS   (Finland)

Author keywords

Benchmark regulation; Clustering; Data envelopment analysis (DEA); Electricity distribution; Productive efficiency; Stochastic non smooth envelopment of data (StoNED)

Indexed keywords

BENCHMARKING; CLUSTER ANALYSIS; DECISION MAKING; ELECTRIC UTILITIES; ENERGY POLICY; STOCHASTIC SYSTEMS;

BENCHMARK REGULATION; CLUSTERINGS; CNLS; CRS; DATA ENVELOPMENT ANALYSE; DECISION MAKING UNIT; ELECTRICITY DISTRIBUTION; NMM; PRODUCTIVE EFFICIENCIES; SFA; STOCHASTIC NON-SMOOTH ENVELOPMENT OF DATA; STOCHASTICS; VRS;

DATA ENVELOPMENT ANALYSIS;

EID: 84879585988     PISSN: 03050483     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.omega.2013.05.007     Document Type: Article

References (52)

1. Farrell M.J. The measurement of productive efficiency. Journal of the Royal Statistical Society 1957, 120(3):253-282.
2. Charnes A., Cooper W.W., Rhodes E. Measuring the inefficiency of decision making units. European Journal of Operational Research 1978, 2(6):429-444.
3. Thanassoulis E, Portela MCAS, Despic O. DEA-the mathematical programming approach to efficiency analysis. In: The measurement of productive efficiency and productivity growth. Oxford University Press; 2008.
4. Liu J.S., Lu L.Y.Y., Lu W., Lin B.J.Y. A survey of DEA applications. Omega 2013, 41(5):893-902.
5. Paradi J.C., Zhu H. A survey on bank branch efficiency and performance research with data envelopment analysis. Omega 2013, 41(1):61-79.
6. Bell R.A., Morey R.C. The search for appropriate benchmarking partners. a macro approach and application to corporate travel management. Omega 1994, 22(5):477-490.
7. Cook W.D., Seiford L.M., Zhu J. Models for performance benchmarking. measuring the effect of e-business activities on banking performance. Omega 2004, 32(4):313-322.
8. Hinojosa M.A., Mármol A.M. Axial solutions for multiple objective linear problems. An application to target setting in DEA models with preferences. Omega 2011, 39(2):159-167.
9. Epure M., Kerstens K., Prior D. Technology-based total factor productivity and benchmarking. new proposals and an application. Omega 2011, 39(6):608-619.
10. Adler N., Liebert V., Yazhemsky E. Benchmarking airports from a managerial perspective. Omega 2013, 41(2):442-458.
11. Samoilenko S., Osei-Bryson K. Using data envelopment analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations. design and implementation of a decision support system. Omega 2013, 41(1):131-142.
12. Aigner D.J., Lovell C.A.K., Schmidt P. Formulation and estimation of stochastic frontier models. J. Econometrics 1977, 6:21-37.
13. Meeusen W., van den Broeck J. Efficiency estimation from Cobb-Douglas production function with composed error. International Economic Review 1977, 8:435-444.
14. Kuosmanen T., Kortelainen M. Stochastic non-smooth envelopment of data. semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis 2012, 38(1):11-28.
15. Kuosmanen T. Stochastic semi-nonparametric frontier estimation of electricity distribution networks. application of the StoNED method in the Finnish regulatory model. Energy Economics 2012, 34:2189-2199.
16. Johnson A.L., Kuosmanen T. One-stage and two-stage DEA estimation of the effects of contextual variables. European Journal of Operational Research 2012, 220(2):559-570.
17. Johnson A.L., Kuosmanen T. One-stage estimation of the effects of operational conditions and practices on productive performance. asymptotically normal and efficient, root-n consistent StoNEZD method. Journal of Productivity Analysis 2011, 36(2):219-230.
18. Orea L., Kumbhakar S.C. Efficiency measurement using a latent class stochastic frontier model. Empirical Economics 2004, 29(1):169-183.
19. O'Donnell C.J., Rao D.S.P., Battese G.E. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 2008, 34:231-255.
20. Po R., Guh Y., Yang M. A new clustering approach using data envelopment analysis. European Journal of Operational Research 2009, 276-284.
21. Triantis K., Sarayia D., Seaver B. Using multivariate methods to incorporate environmental variables for local and global efficiency performance analysis. INFOR. Information Systems and Operational Research 2010, 48(1/2):39-52.
22. Fallah-Fini S., Triantis K., de la Garza J.M., Seaver W. Measuring the efficiency of highway maintenance contracting strategies. a bootstrapped non-parametric meta-frontier approach. European Journal of Operational Research 2012, 219(1):134-145.
23. Banker R.D. Maximum likelihood, consistency and data envelopment analysis. a statistical foundation. Management Science 1993, 39(10):1265-1273.
24. Kuosmanen T., Johnson A.L. Data envelopment analysis as nonparametric least-squares regression. Operations Research 2010, 149-160.
25. Aparicio J., Ruiz J.L., Sirvent I. Closest targets and minimum distance to the Pareto-efficient frontier in DEA. Journal of Productivity Analysis 2007, 28:209-218.
26. Aparicio J, Pastor JT. On how to properly calculate the Euclidean distance-based measure in DEA. Optimization 2012 http://dx.doi.org/10.1080/02331934.2012.655692.
27. Johnson S.C. Hierarchical clustering schemes. Psychometrika 1967, 32(3):241-254.
28. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning. data mining, inference, and prediction 2009, Springer Science and Business Media, New York, USA.
29. Ward J.H. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 1963, 58(301):234-244.
30. Mount D.W. Bioinformatics. sequence and genome analysis 2004, John Inglis, New York. 2nd ed.
31. McGarigal K., Cushman S., Stafford S.G. Multivariate statistics for wildlife and ecology research 2000, Springer-Verlag, New York.
32. Causton H.C., Quackenbush J., Brazma A. Microarray/gene expressions data analysis. a beginner's guide 2003, Blackwell Science, UK.
33. Russell D. The principles of computer networking 1989, Cambridge University Press, Cambridge, UK.
34. Steinbach M, Karypis G, Kumar V. A comparison of document clustering techniques. In: KDD-2000 workshop on text mining, 2000.
35. Fraley C., Raftery A.E. Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 2002, 97:611-631.
36. MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, University of California Press; 1967. p. 281-97.
37. Pelleg D, Moore A. X-means: extending K-means with efficient estimation of the number of clusters. In: ICML 2000. Morgan Kaufmann; 2000. p. 727-34.
38. Melin P., Castillo O. Hybrid intelligent systems for pattern recognition using soft computing. an evolutionary approach for neural networks and fuzzy systems 2005, Springer-Verlag, Berlin, Germany.
39. Heyer L.J., Kruglyak S., Yooseph S. Exploring expression data. identification and analysis of coexpressed genes. Genome Research 1999, 9(11):1106-1115.
40. Kaufman L., Rousseeuw P.J. Finding groups in data. an introduction to cluster analysis 1990, Wiley, New York.
41. McLachlan G.J., Peel D. Finite mixture models 2000, John Wiley and Sons, New York, USA.
42. Medvedovic M., Sivaganesan S. Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 2002, 18(9):1194-1206.
43. Vaithyanathan S, Dom B. Model-based hierarchical clustering. In: IPDPS'03. Morgan Kaufmann Publishers; 2003. p. 599-608.
44. Zhong S., Ghosh J. A unified framework for model- based clustering. Journal of Machine Learning Research 2003, 4:1001-1037.
45. Smyth P. Model selection for probabilistic clustering using cross-validated likelihood. Statistics and Computing 2000, 9(1):63-72.
46. Ji Y., Wu C., Liu P., Wang J., Coombes R.K. Applications of beta-mixture models in bioinformatics. Bioinformatics 2005, 21(9):2118-2122.
47. Akaike H. A new look at the statistical identification model. IEEE Transactions on Automatic Control 1974, 19:716-723.
48. Schwarz G. Estimating the dimension of a model. Annals of Statistics 1978, 6(2):461-464.
49. Bozdogan H. Model selection and Akaike I nformation Criterion (AIC). the general theory and its analytic extensions. Psychometrika 1987, 52(3):345-370.
50. Biernacki C., Govaert G. Choosing models in model-based clustering and discriminant analysis. Journal of Statistical Computation and Simulation 1999, 64(1):49-71.
51. Pan W. Incorporating gene functions as priors in model-based clustering of microarray gene expression data. Bioinformatics 2006, 22(7):795-801.
52. Seiford L.M., Zhu J. Context-dependent data envelopment analysis. measuring attractiveness and progress. Omega 2003, 31:397-408.