Mostrar el registro sencillo del ítem

dc.contributor.authorVillanueva-Cantillo, Jeyms
dc.contributor.authorMunoz-Marquez, Manuel
dc.date.accessioned2021-07-22T17:01:41Z
dc.date.available2021-07-22T17:01:41Z
dc.date.issued2021
dc.identifier.issn03772217
dc.identifier.urihttps://hdl.handle.net/20.500.12442/8028
dc.description.abstractThe selection of input and output variables is a key step in evaluating the relative efficiency of decision- making units (DMUs) in data envelopment analysis (DEA). In this paper, we present a methodology based on Monte Carlo simulations and bootstrapping for calculating the critical values of relevance measures in variable selection methods in DEA. Additionally, we define a set of metrics to study the methods’ performance when using such critical values. We conducted an extensive simulation study, applying the proposed methodology to two variable selection methods in 28 single-output model specifications (i.e., different number of inputs and DMUs in the DEA model) under multiple scenarios, varying factors related to the functional form of the production function, the probability of an input being relevant in the model, the probability distribution of the inputs, and the theoretical efficiencies of the DMUs. The simulation study shows that (i) our proposed methodology yields consistent results for the two methods studied, in terms of the generated critical values and the performance metrics, and (ii) for most model specifications, the critical values can be estimated with a linear model with a high adjusted R 2 , using factors related to the input probability distribution and the probability of an input being relevant as independent variables. Furthermore, we describe and compare the performance of the two methods studied, provide guidelines for using our methodology and the results presented in this paper, and propose suggestions for future research.eng
dc.format.mimetypepdfspa
dc.language.isoengeng
dc.publisherElsevierspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceEuropean Journal of Operational Research (EJOR)eng
dc.sourceVol. 290, Issue 2 (2021)eng
dc.subjectData envelopment analysiseng
dc.subjectVariable selectioneng
dc.subjectCritical valueseng
dc.subjectMonte Carlo simulationseng
dc.titleMethodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysiseng
dcterms.referencesAdler, N., & Golany, B. (2001). Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an ap- plication to western europe. European Journal of Operational Research, 132 (2), 18–31. https://doi.org/10.1016/S0377-2217(0 0)0 0150-8 .eng
dcterms.referencesAdler, N., & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53 (9), 985–991. https://doi.org/10.1057/palgrave.jors.2601400 .eng
dcterms.referencesAdler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction. European Journal of Operational Re- search, 202 (1), 273–284. https://doi.org/10.1016/j.ejor.2009.03.050 .eng
dcterms.referencesAmirteimoori, A., Despotis, D., & Kordrostami, S. (2012). Variable reduction in data envelopment analysis. Optimization, 63 (5), 735–745. https://doi.org/10. 1080/02331934.2012.684354eng
dcterms.referencesBanker, R. D. (1996). Hypothesis tests using data envelopment analysis. The Journal of Productivity Analysis, 7 (2/3), 139–159. https://doi.org/10.10 07/BF0 0157038 .eng
dcterms.referencesCharnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2 (6), 429–4 4 4. https:// doi.org/10.1016/0377-2217(78)90138-8 .eng
dcterms.referencesDaraio, C. , & Simar, L. (2007). Advanced robust and nonparametric methods in effi- ciency analysis: methodology and applications (1st). Springer Science + Business Media .eng
dcterms.referencesDyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132 , 245–259. https://doi.org/10.1016/S0377-2217(0 0)0 0149-1 .eng
dcterms.referencesEskelinen, J. (2017). Comparison of variable selection techniques for data envelop- ment analysis in a retail bank. European Journal of Operational Research, 259 (2), 778–788. https://doi.org/10.1016/j.ejor.2016.11.009 .eng
dcterms.referencesFanchon, P. (2003). Variable selection for dynamic measures of efficiency in the computer industry. International Advances in Economic Research, 9 (3), 175–188. https://doi.org/10.1007/BF02295441 .eng
dcterms.referencesFernandez-Palacin, F., Lopez-Sanchez, M. A., & Munoz-Marquez, M. (2017). Step- wise selection of variables in DEA using contribution loads. Pesquisa Operacional, 38 (1), 31–52. https://doi.org/10.1590/0101-7438.2018.038.01.0031 .eng
dcterms.referencesHolland, D., & Lee, S. (2002). Impacts of random noise and specification on es- timates of capacity derived from data envelopment analysis. European Journal of Operational Research, 137 (1), 10–21. https://doi.org/10.1016/S0377-2217(01) 0 0 087-X .eng
dcterms.referencesJenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Oper- ational Research, 147 (1), 51–61. https://doi.org/10.1016/S0377- 2217(02)00243- 6 .eng
dcterms.referencesJitthavech, J. (2016). Variable elimination in nested DEA models: a statistical ap- proach. International Journal of Operational Research, 27 (3), 389–410. https://doi. org/10.1504/IJOR.2016.078945 .eng
dcterms.referencesKao, L.-J., Lu, C.-J., & Chiu, C. C. (2011). Efficiency measurement using independent component analysis and data envelopment analysis. European Journal of Opera- tional Research, 210 (2), 310–317. https://doi.org/10.1016/j.ejor.2010.09.016 .eng
dcterms.referencesLi, Y., & Liang, L. (2010). A shapley value index on the importance of variables in DEA models. Expert Systems with Applications, 37 (9), 6287–6292. https://doi.org/ 10.1016/j.eswa.2010.02.093 .eng
dcterms.referencesLi, Y., Shi, X., Yang, M., & Liang, L. (2017). Variable selection in data envelopment analysis via akaike’s information criteria. Annals of Operations Research, 253 (1), 453–476. https://doi.org/10.1007/s10479- 016- 2382- 2 .eng
dcterms.referencesLimleamthong, P., & Guillén-Gosálbez, G. (2018). Mixed-integer programming ap- proach for dimensionality reduction in data envelopment analysis: Application to the sustainability assessment of technologies and solvents. Industrial & Engi- neering Chemistry Research, 57 (30), 9866–9878. https://doi.org/10.1021/acs.iecr. 7b05284 .eng
dcterms.referencesLin, T.-Y., & Chiu, S. H. (2013). Using independent component analysis and network DEA to improve bank performance evaluation. Economic Modelling, 32 (1), 608–616. https://doi.org/10.1016/j.econmod.2013.03.003 .eng
dcterms.referencesLiu, J. S., Lu, L. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega, 58 , 33–45. https://doi.org/10.1016/j.omega.2015.04.004 .eng
dcterms.referencesMadhanagopal, R., & Chandrasekaran, R. (2014). Selecting appropriate variables for dea using genetic algorithm (ga) search procedure. International Journal of Data Envelopment Analysis and ∗Operations Research ∗, 1 (2), 28–33. https://doi.org/10. 12691/ijdeaor- 1- 2- 3 .eng
dcterms.referencesMorita, H., & Avkiran, N. K. (2009). Selecting inputs and outputs in data envelop- ment analysis by designing statistical experiments. Journal of the Operations Re- search Society of Japan, 52 (2), 163–173. https://doi.org/10.15807/jorsj.52.163 .eng
dcterms.referencesNataraja, N. R., & Johnson, A. L. (2011). Guidelines for using variable selection tech- niques in data envelopment analysis. European Journal of Operational Research, 215 (3), 662–669. https://doi.org/10.1016/j.ejor.2011.06.045 .eng
dcterms.referencesPastor, J. T., Ruiz, J. L., & Sirvent, I. (2002). A statistical test for nested radial DEA models. Inmaculada Operations Research, 50 (4), 728–735. https://doi.org/10.1287/ opre.50.4.728.2866 .eng
dcterms.referencesPerelman, S., & Santín, D. (2009). How to generate regularly behaved production data? a monte carlo experimentation on DEA scale efficiency measurement. Eu- ropean Journal of Operational Research, 199 (1), 303–310. https://doi.org/10.1016/j. ejor.2008.11.013 .eng
dcterms.referencesR Core Team (2017). R: A language and environment for statistical computing, Vienna, Austria . https://www.R-project.org/eng
dcterms.referencesRuggiero, J. (2005). Impact assessment of input omission on DEA. International Jour- nal of Information Technology & Decision Making, 4 (3), 359–368. https://doi.org/ 10.1142/S02196220 050 0160X .eng
dcterms.referencesSexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: cri- tique and extensions. New Directions for Program Evaluation, 1986 (32), 73–105. https://doi.org/10.1002/ev.1441 .eng
dcterms.referencesSharma, M. J., & Yu, S. J. (2015). Stepwise regression data envelopment analysis for variable reduction. Applied Mathematics and Computation, 253 , 126–134. https: //doi.org/10.1016/j.amc.2014.12.050 .eng
dcterms.referencesSimar, L., & Wilson, P. W. (2001). Testing restrictions in nonparametric efficiency models. Communications in Statistics –Simulation and Computation, 30 (1), 159–184. https://doi.org/10.1081/SAC-10 0 0 01865 .eng
dcterms.referencesSirvent, I., Ruiz, J. L., Borra ´s, F., & Pastor, J. T. (2005). A monte carlo evaluation of several tests for the selection of variables in DEA models. International Journal of Information Technology & Decision Making, 4 (3), 325–343. https://doi.org/10. 1142/S02196220 050 01581 .eng
dcterms.referencesToloo, M., & Babaee, S. (2015). On variable reductions in data envelopment analy- sis with an illustrative application to a gas company. Applied Mathematics and Computation, 270 , 527–533. https://doi.org/10.1016/j.amc.2015.06.122 .eng
dcterms.referencesToloo, M., Barat, M., & Masoumzadeh, A. (2015). Selective measures in data envel- opment analysis. Annals of Operations Research, 226 (1), 623–642. https://doi.org/ 10.1007/s10479- 014- 1714- 3 .eng
dcterms.referencesToloo, M., & Tichý, T. (2015). Two alternative approaches for selecting performance measures in data envelopment analysis. Measurement, 65 , 29–40. https://doi. org/10.1016/j.measurement.2014.12.043 .eng
dcterms.referencesUeda, T., & Hoshiai, Y. (1997). Application of principal component analysis for parsi- monious summarization of DEA inputs and/or outputs. Journal of the Operations Research Society of Japan, 40 (4), 466–478. https://doi.org/10.15807/jorsj.40.466 .eng
dcterms.referencesWagner, J. M., & Shimshak, D. G. (2007). Stepwise selection of variables in data en- velopment analysis: Procedures and managerial perspectives. European Journal of Operational Research, 180 (1), 57–67. https://doi.org/10.1016/j.ejor.2006.02.048 .eng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versioninfo:eu-repo/semantics/publishedVersionspa
dc.type.driverinfo:eu-repo/semantics/articleeng
dc.identifier.doihttps://doi.org/10.1016/j.ejor.2020.08.021
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0377221720307293
dc.type.spaArtículo científicospa


Ficheros en el ítem

Thumbnail
Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos
    Artículos científicos evaluados por pares

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional