The origins and development of statistical approaches in non-parametric frontier

Moradi-Motlagh, A., and A. Emrouznejad (2022) The origins and development of statistical approaches in non-parametric frontier models: A survey of the first two decades of scholarly literature (1998-2020). Annals of Operations Research, 318: 713–741. https://doi.org/10.1007/s10479-023-05169-w. 

 

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List of papers on Data Envelopment Analysis Bootstrapping:

  • Agrell, P. J., Mattsson, P., & Månsson, J. (2020). Impacts on efficiency of merging the Swedish district courts. Annals of Operations Research, 288(2), 653-679.
  • Alberta Oliveira, M., & Santos, C. (2005). Assessing school efficiency in Portugal using FDH and bootstrapping. Applied Economics, 37(8), 957-968.
  • Álvarez, I., Barbero, J., & Zofío, J. L. (2016). A data envelopment analysis toolbox for MATLAB. Journal of Statistical Software, 95.
  • Andersson, C., Antelius, J., Månsson, J., & Sund, K. (2017). Technical efficiency and productivity for higher education institutions in Sweden. Scandinavian Journal of Educational Research, 61(2), 205-223.
  • Aragon, Y., Daouia, A., & Thomas-Agnan, C. (2005). Nonparametric frontier estimation: a conditional quantile-based approach. Econometric Theory, 21(2), 358-389.
  • Atwood, J., & Shaik, S. (2015). Package ‘DEAboot’
  • Bădin, L., Daraio, C., & Simar, L. (2010). Optimal bandwidth selection for conditional efficiency measures: a data-driven approach. European Journal of Operational Research, 201(2), 633-640.
  • Bădin, L., Daraio, C., & Simar, L. (2012). How to measure the impact of environmental factors in a nonparametric production model. European Journal of Operational Research, 223(3), 818-833.
  • Bădin, L., Daraio, C., & Simar, L. (2014). Explaining inefficiency in nonparametric production models: the state of the art. Annals of Operations Research, 214(1), 5-30.
  • Badunenko, O., & Tauchmann, H. (2018). SIMARWILSON: Stata module to perform Simar & Wilson (2007) efficiency analysis.
  • Baier-Fuentes, H., Merigó, J. M., Amorós, J. E., & Gaviria-Marín, M. (2019). International entrepreneurship: a bibliometric overview. International Entrepreneurship and Management Journal, 15(2), 385-429.
  • Banker, R., & Maindiratta, A. (1992). Maximum likelihood estimation of monotone and concave production frontiers. Journal of Productivity Analysis, 3(4), 401-415.
  • Banker, R., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56(1), 48-58.
  • Banker, R., Natarajan, R., & Zhang, D. (2019). Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: second stage OLS versus bootstrap approaches. European Journal of Operational Research, 278(2), 368-384.
  • Boame, A. K. (2004). The technical efficiency of Canadian urban transit systems. Transportation Research Part E: Logistics and Transportation Review, 40(5), 401-416.
  • Bogetoft, P., Otto, L., & Otto, M. L. (2019). Package ‘benchmarking’.
  • Broadus, R. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5-6), 373-379.
  • Brümmer, B. (2001). Estimating confidence intervals for technical efficiency: the case of private farms in Slovenia. European Review of Agricultural Economics, 28(3), 285-306.
  • Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Information (International Social Science Council), 22(2), 191-235.
  • Cazals, C., Florens, J.-P., & Simar, L. (2002). Nonparametric frontier estimation: a robust approach. Journal of Econometrics, 106(1), 1-25.
  • Chambers, R. G., Chung, Y., & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98(2), 351-364.
  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
  • Cheng, G., & Qian, Z. (2014). MaxDea pro 6.3 manual. Beijing: Beijing Realworld Software Company Ltd.
  • Cooper, W. W., Huang, Z., Lelas, V., Li, S. X., & Olesen, O. B. (1998). Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. Journal of Productivity Analysis, 9(1), 53-79.
  • Daouia, A., & Simar, L. (2007). Nonparametric efficiency analysis: a multivariate conditional quantile approach. Journal of Econometrics, 140(2), 375-400.
  • Daouia, A., Simar, L., & Wilson, P. W. (2017). Measuring firm performance using nonparametric quantile-type distances. Econometric Reviews, 36(1-3), 156-181.
  • Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: a probabilistic approach. Journal of Productivity Analysis, 24(1), 93-121.
  • Daraio, C., & Simar, L. (2007a). Advanced robust and nonparametric methods in efficiency analysis: Methodology and applications: Springer Science & Business Media.
  • Daraio, C., & Simar, L. (2007b). Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach. Journal of Productivity Analysis, 28(1-2), 13-32.
  • Daraio, C., & Simar, L. (2014). Directional distances and their robust versions: Computational and testing issues. European Journal of Operational Research, 237(1), 358-369.
  • Daraio, C., Simar, L., & Wilson, P. W. (2010). Testing whether two-stage estimation is meaningful in non-parametric models of production. Universite Catholique De Louvain (Discussion Paper).
  • Daraio, C., Simar, L., & Wilson, P. W. (2018). Central limit theorems for conditional efficiency measures and tests of the ‘separability’condition in non‐parametric, two‐stage models of production. The Econometrics Journal, 21(2), 170-191.
  • Daraio, C., Simar, L., & Wilson, P. W. (2019). Fast and efficient computation of directional distance estimators. Annals of Operations Research, 1-31.
  • Darairo, C., Kerstens, K., Nepomuceno, T. C. C., & Sickles, R. C. (2019). Productivity and efficiency analysis software: an exploratory bibliographical survey of the options. Journal of Economic Surveys, 33(1), 85-100.
  • Davidova, S., & Latruffe, L. (2007). Relationships between technical efficiency and financial management for Czech Republic farms. Journal of Agricultural Economics, 58(2), 269-288.
  • De Witte, K., & Marques, R. C. (2010). Designing performance incentives, an international benchmark study in the water sector. Central European Journal of Operations Research, 18(2), 189-220.
  • Deprins, D., Simar, L., & Tulkens, H. (2006). Measuring labor-efficiency in post offices Public goods, environmental externalities and fiscal competition (pp. 285-309): Springer.
  • Eck, N. v., & Waltman, L. (2020). VOSviewer Manual: Manual for VOSviewer Version 1.6. 14: Leiden: CWTS.
  • Efron, B. (1992). Bootstrap methods: another look at the jackknife Breakthroughs in statistics (pp. 569-593): Springer.
  • Eling, M., & Luhnen, M. (2010). Efficiency in the international insurance industry: A cross-country comparison. Journal of Banking & Finance, 34(7), 1497-1509.
  • Emrouznejad, A., & Thanassoulis, E. (2005). Performance improvement management. DEASoft, PIM Ltd.
  • Emrouznejad, A., & Yang, G.-l. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.
  • Essid, H., Ouellette, P., & Vigeant, S. (2014). Productivity, efficiency, and technical change of Tunisian schools: A bootstrapped Malmquist approach with quasi-fixed inputs. Omega, 42(1), 88-97.
  • Färe, R., & Grosskopf, S. (2006). New directions: efficiency and productivity (Vol. 3): Springer Science & Business Media.
  • Fukuyama, H., & Tan, Y. (2021). Corporate social behaviour: Is it good for efficiency in the Chinese banking industry? Annals of Operations Research, 306(1), 383-413.
  • Galariotis, E., Kosmidou, K., Kousenidis, D., Lazaridou, E., & Papapanagiotou, T. (2021). Measuring the effects of M&As on Eurozone bank efficiency: An innovative approach on concentration and credibility impacts. Annals of Operations Research, 306(1), 343-368.
  • Halkos, G. E., & Tzeremes, N. G. (2013). Estimating the degree of operating efficiency gains from a potential bank merger and acquisition: A DEA bootstrapped approach. Journal of Banking & Finance, 37(5), 1658-1668.
  • Hatami-Marbini, A., Emrouznejad, A., & Tavana, M. (2011). A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. European Journal of Operational Research, 214(3), 457-472.
  • Hawdon, D. (2003). Efficiency, performance and regulation of the international gas industry—a bootstrap DEA approach. Energy Policy, 31(11), 1167-1178.
  • Johnes, J. (2006). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review, 25(3), 273-288.
  • Kaffash, S., & Marra, M. (2017). Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds. Annals of Operations Research, 253(1), 307-344.
  • Kao, C. (2014). Network data envelopment analysis: a review. European Journal of Operational Research, 239(1), 1-16.
  • Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric theory, 783-793.
  • Kneip, A., Simar, L., & Wilson, P. W. (2008). Asymptotics and consistent bootstraps for DEA estimators in nonparametric frontier models. Econometric Theory, 1663-1697.
  • Kneip, A., Simar, L., & Wilson, P. W. (2015). When bias kills the variance: Central limit theorems for DEA and FDH efficiency scores. Econometric Theory, 394-422.
  • Kneip, A., Simar, L., & Wilson, P. W. (2016). Testing hypotheses in nonparametric models of production. Journal of Business & Economic Statistics, 34(3), 435-456.
  • Kohl, S., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2019). The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, 22(2), 245-286.
  • Laengle, S., Merigó, J. M., Miranda, J., Słowiński, R., Bomze, I., Borgonovo, E., . . . Teunter, R. (2017). Forty years of the European Journal of Operational Research: A bibliometric overview. European Journal of Operational Research, 262(3), 803-816.
  • Lampe, H. W., & Hilgers, D. (2015). Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA. European Journal of Operational Research, 240(1), 1-21.
  • Liu, J. S., Lu, L. Y., Lu, W.-M., & Lin, B. J. (2013). A survey of DEA applications. Omega, 41(5), 893-902.
  • Lothgren, M., & Tambour, M. (1999). Bootstrapping the data envelopment analysis Malmquist productivity index. Applied Economics, 31(4), 417-425.
  • McDonald, J. (2009). Using least squares and tobit in second stage DEA efficiency analyses. European Journal of Operational Research, 197(2), 792-798.
  • Merigó, J. M., Pedrycz, W., Weber, R., & de la Sotta, C. (2018). Fifty years of Information Sciences: A bibliometric overview. Information Sciences, 432, 245-268.
  • Merkert, R., & Hensher, D. A. (2011). The impact of strategic management and fleet planning on airline efficiency–A random effects Tobit model based on DEA efficiency scores. Transportation Research Part A: Policy and Practice, 45(7), 686-695.
  • Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228.
  • Moradi-Motlagh, A., & Babacan, A. (2015). The impact of the global financial crisis on the efficiency of Australian banks. Economic Modelling, 46, 397-406.
  • Muhuri, P. K., Shukla, A. K., & Abraham, A. (2019). Industry 4.0: A bibliometric analysis and detailed overview. Engineering Applications of Artificial Intelligence, 78, 218-235.
  • Olesen, O. B., & Petersen, N. (1995). Chance constrained efficiency evaluation. Management Science, 41(3), 442-457.
  • Olesen, O. B., & Petersen, N. C. (2016). Stochastic data envelopment analysis – A review. [Review]. European Journal of Operational Research, 251(1), 2-21.
  • Park, B. U., Simar, L., & Weiner, C. (2000). FDH efficiency scores from a stochastic point of view. Econometric Theory, 16, 855-877.
  • Peters, H., & Van Raan, A. (1991). Structuring scientific activities by co-author analysis: An expercise on a university faculty level. Scientometrics, 20(1), 235-255.
  • Porembski, M., Breitenstein, K., & Alpar, P. (2005). Visualizing efficiency and reference relations in data envelopment analysis with an application to the branches of a German bank. Journal of Productivity Analysis, 23(2), 203-221.
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349.
  • Rossi, M. n. A., & Ruzzier, C. A. (2000). On the regulatory application of efficiency measures. Utilities Policy, 9(2), 81-92.
  • Salim, R., Arjomandi, A., & Seufert, J. H. (2016). Does corporate governance affect Australian banks’ performance? Journal of International Financial Markets, Institutions and Money, 43, 113-125.
  • Simar, L. (1996). Aspects of statistical analysis in DEA-type frontier models. Journal of Productivity Analysis, 7(2), 177-185.
  • Simar, L. (2003). Detecting outliers in frontier models: A simple approach. Journal of Productivity Analysis, 20(3), 391-424.
  • Simar, L., & Vanhems, A. (2012). Probabilistic characterization of directional distances and their robust versions. Journal of Econometrics, 166(2), 342-354.
  • Simar, L., Vanhems, A., & Wilson, P. W. (2012). Statistical inference for DEA estimators of directional distances. European Journal of Operational Research, 220(3), 853-864.
  • Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49-61.
  • Simar, L., & Wilson, P. W. (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operational Research, 115(3), 459-471.
  • Simar, L., & Wilson, P. W. (2000a). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779-802.
  • Simar, L., & Wilson, P. W. (2000b). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13(1), 49-78.
  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31-64.
  • Simar, L., & Wilson, P. W. (2011a). Inference by the m out of n bootstrap in nonparametric frontier models. Journal of Productivity Analysis, 36(1), 33-53.
  • Simar, L., & Wilson, P. W. (2011b). Two-stage DEA: caveat emptor. Journal of Productivity Analysis, 36(2), 205-218.
  • Simar, L., & Wilson, P. W. (2015). Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour. International Statistical Review, 83(1), 77-110.
  • Simar, L., & Wilson, P. W. (2020). Technical, allocative and overall efficiency: Estimation and inference. European Journal of Operational Research, 282(3), 1164-1176.
  • Simm, J., Besstremyannaya, G., & Simm, M. (2016). Package ‘rDEA’.
  • Tiemann, O., & Schreyögg, J. (2009). Effects of ownership on hospital efficiency in Germany. Business Research, 2(2), 115-145.
  • Tiemann, O., & Schreyögg, J. (2012). Changes in hospital efficiency after privatization. Health Care Management Science, 15(4), 310-326.
  • Tortosa-Ausina, E. (2002). Bank cost efficiency and output specification. Journal of Productivity Analysis, 18(3), 199-222.
  • Türkeli, S., Kemp, R., Huang, B., Bleischwitz, R., & McDowall, W. (2018). Circular economy scientific knowledge in the European Union and China: A bibliometric, network and survey analysis (2006–2016). Journal of Cleaner Production, 197, 1244-1261.
  • Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
  • Van Leeuwen, T. (2004). Descriptive versus evaluative bibliometrics Handbook of Quantitative Science and Technology Research (pp. 373-388): Springer.
  • Wilson, P. W. (1993). Detecting outliers in deterministic nonparametric frontier models with multiple outputs. Journal of Business & Economic Statistics, 11(3), 319-323.
  • Wilson, P. W. (1995). Detecting influential observations in data envelopment analysis. Journal of Productivity Analysis, 6(1), 27-45.
  • Wilson, P. W. (2008). FEAR: A software package for frontier efficiency analysis with R. Socio-economic Planning Sciences, 42(4), 247-254.
  • Witte, K. D., & López-Torres, L. (2017). Efficiency in education: a review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339-363.
  • Worthington, A. C., & Lee, B. L. (2008). Efficiency, technology and productivity change in Australian universities, 1998–2003. Economics of Education Review, 27(3), 285-298.
  • Yang, L., & Zhang, X. (2018). Assessing regional eco-efficiency from the perspective of resource, environmental and economic performance in China: A bootstrapping approach in global data envelopment analysis. Journal of Cleaner Production, 173, 100-111.
  • Yeung, W., Goto, T. K., & Leung, W. K. (2017). A bibliometric review of research trends in neuroimaging. Current Science, 112(4), 725-734.
  • Zhu, J. (2014). DEAFrontier: Worchestpr.