How to cite this paper
Sakouvogui, K. (2019). Banks performance evaluation: A hybrid DEA-SVM- The case of U.S. agricultural banks.Accounting, 5(3), 107-120.
Refrences
Andrews, D. F., & Pregibon, D. (1978). Finding the outliers that matter. Journal of the Royal Statistical Society. Series B (Methodological), 40, 85-93.
Ataullah, A., & Le, H. (2006). Economic reforms and bank efficiency in developing countries: the case of the Indian banking industry. Applied Financial Economics, 16(9), 653-663.
Azadeh, A., Ghaderi, S. F., Anvari, M., & Saberi, M. (2007). Performance assessment of electric power generations using an adaptive neural network algorithm. Energy Policy, 35(6), 3155-3166.
Banker, R. D., & Chang, H. (2006). The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research, 175(2), 1311-1320.
Banker, R. D., Gadh, V. M., & Gorr, W. L. (1993). A Monte Carlo comparison of two production frontier estimation methods: corrected ordinary least squares and data envelopment analysis. European Journal of Operational Research, 67(3), 332-343.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Boyacioglu, M. A., Kara, Y., & Baykan, Ö. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355-3366.
Cao, L. J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), 1506-1518.
Casu, B., & Molyneux, P. (2003). A comparative study of efficiency in European banking. Applied Economics, 35(17), 1865-1876.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Drake, L. (2001). Efficiency and productivity change in UK banking. Applied Financial Economics, 11(5), 557-571.
Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: a non-parametric approach. Applied economics, 27(4), 385-395.
Fried, H. O., Lovell, C. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of productivity Analysis, 17(1-2), 157-174.
Hao, J., Hunter, W. C., & Yang, W. K. (2001). Deregulation and efficiency: the case of private Korean banks. Journal of Economics and Business, 53(2-3), 237-254.
Holland, D. S., & Lee, S. T. (2002). Impacts of random noise and specification on estimates of capacity derived from data envelopment analysis. European Journal of Operational Research, 137(1), 10-21.
Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of econometrics, 54(1-3), 159-178.
Martić, M., & Savić, G. (2001). An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development. European Journal of Operational Research, 132(2), 343-356.
Ondrich, J., & Ruggiero, J. (2002). Outlier detection in data envelopment analysis: an analysis of jackknifing. Journal of the Operational Research Society, 53(3), 342-346.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
Sealey Jr, C. W., & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The journal of finance, 32(4), 1251-1266.
Simar, L., & Zelenyuk, V. (2011). Stochastic FDH/DEA estimators for frontier analysis. Journal of Productivity Analysis, 36(1), 1-20.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.
Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation. Journal of Econometrics, 99(1), 39-61.
Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer, 1995.
Vapnik, V. (1998). Statistical Learning Theory. Wiley, New York.
Wu, D. D., Yang, Z., & Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert systems with applications, 31(1), 108-115.
Xu, Y., Zomer, S., & Brereton, R. G. (2006). Support vector machines: a recent method for classification in chemometrics. Critical Reviews in Analytical Chemistry, 36(3-4), 177-188.
Ataullah, A., & Le, H. (2006). Economic reforms and bank efficiency in developing countries: the case of the Indian banking industry. Applied Financial Economics, 16(9), 653-663.
Azadeh, A., Ghaderi, S. F., Anvari, M., & Saberi, M. (2007). Performance assessment of electric power generations using an adaptive neural network algorithm. Energy Policy, 35(6), 3155-3166.
Banker, R. D., & Chang, H. (2006). The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research, 175(2), 1311-1320.
Banker, R. D., Gadh, V. M., & Gorr, W. L. (1993). A Monte Carlo comparison of two production frontier estimation methods: corrected ordinary least squares and data envelopment analysis. European Journal of Operational Research, 67(3), 332-343.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Boyacioglu, M. A., Kara, Y., & Baykan, Ö. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355-3366.
Cao, L. J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), 1506-1518.
Casu, B., & Molyneux, P. (2003). A comparative study of efficiency in European banking. Applied Economics, 35(17), 1865-1876.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Drake, L. (2001). Efficiency and productivity change in UK banking. Applied Financial Economics, 11(5), 557-571.
Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: a non-parametric approach. Applied economics, 27(4), 385-395.
Fried, H. O., Lovell, C. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of productivity Analysis, 17(1-2), 157-174.
Hao, J., Hunter, W. C., & Yang, W. K. (2001). Deregulation and efficiency: the case of private Korean banks. Journal of Economics and Business, 53(2-3), 237-254.
Holland, D. S., & Lee, S. T. (2002). Impacts of random noise and specification on estimates of capacity derived from data envelopment analysis. European Journal of Operational Research, 137(1), 10-21.
Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of econometrics, 54(1-3), 159-178.
Martić, M., & Savić, G. (2001). An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development. European Journal of Operational Research, 132(2), 343-356.
Ondrich, J., & Ruggiero, J. (2002). Outlier detection in data envelopment analysis: an analysis of jackknifing. Journal of the Operational Research Society, 53(3), 342-346.
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
Sealey Jr, C. W., & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The journal of finance, 32(4), 1251-1266.
Simar, L., & Zelenyuk, V. (2011). Stochastic FDH/DEA estimators for frontier analysis. Journal of Productivity Analysis, 36(1), 1-20.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.
Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation. Journal of Econometrics, 99(1), 39-61.
Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer, 1995.
Vapnik, V. (1998). Statistical Learning Theory. Wiley, New York.
Wu, D. D., Yang, Z., & Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert systems with applications, 31(1), 108-115.
Xu, Y., Zomer, S., & Brereton, R. G. (2006). Support vector machines: a recent method for classification in chemometrics. Critical Reviews in Analytical Chemistry, 36(3-4), 177-188.