Title page for 92441014


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Student Number 92441014
Author Yi-chun Kuo(ɧg)
Author's Email Address chun@msa.vnu.edu.tw
Statistics This thesis had been viewed 2424 times. Download 353 times.
Department Business Administration
Year 2006
Semester 2
Degree Ph.D.
Type of Document Doctoral Dissertation
Language English
Title The Data Envelopment Analysis Models for the Application of Two-Group Discriminant Ananlysis
Date of Defense 2007-06-08
Page Count 90
Keyword
  • bankruptcy prediction
  • Data envelopment analysis
  • minimized overlap boundary
  • misclassification cost
  • stratified DEA frontier
  • Abstract Discriminant analysis for two-group problem has wide applicability in business environments, such as business failure prediction, credit risk assessment, analysis of the characteristics of different groups of customers and quality control of production system.
    In this dissertation, a nonparametric approach based on the Data Envelopment Analysis (DEA) models is proposed to establish a pair of piecewise discriminant hyperplanes to solve the two-group discriminant problem. The dissertation includes three parts. First part of this study is to identify a minimized overlap boundary of two groups which is a major source of misclassification in discriminant problem. While the overlap boundary can be identified, the decision maker can pay more attention to the new observation which is predicted to appear within the boundary.
    Second part of this study is to propose a novel procedure based on the stratified DEA model for two-group discriminant problems. Differing to most existing discriminant approaches which establish a single hyperplane for classification, a pair of nonlinear discriminant frontiers was constructed by the benchmarks of two groups. The major merit of this novel procedure is that, such nonlinear discriminant frontiers are formed by the benchmarks without the need of pre-specifying the classification function form as other parametric DA approaches do. The efficiency score is then used to be as the measurement for classification and prediction.
    In the third part of this study, the methods and procedures introduced in part one and part two are applied for the application of bankruptcy prediction. In this part, we incorporate the consideration of risk and cost of Type and Type errors to minimize the misclassification cost, which is usually ignored in some approaches using hit-ratio as the indicator of correct classification. Especially in an uneven case, the rule of most approaches tends to have upward biases towards the larger class (the non-bankrupt class) to increase the hit-ratio. Therefore, an asymmetric-stratified DEA model was proposed to deal with the problem while the cost of Type error is substantially greater than Type , because a little sacrifice in hit-ratio of the smaller case (bankrupt) will greatly increase the total misclassification cost.
    Table of Content ABSTRACTKKKKKKKKKKKKKKKKKKKKKKKKK.I
    ACKNOWLEDGMENTKKKKKKKKKKKKKKKKKKKKKK V
    TABLE OF CONTENTSKKKKKKKKKKKKKKKKKKKK VI
    LIST OF FIGURESKKKKKKKKKKKKKKKKKKKK VIII
    LIST OF TABLESKKKKKKKKKKKKKKKKKKKK  IX
    CHAPTER 1 INTRODUCTIONKKKKKKKKKKKKKKKKK  1
    1.1 MotivationKKKKKKKKKKKKKKKKKKKKK  1
    1.2 Research Purposes KKKKKKKKKKKKKKKKK  4
    1.3.Thesis StructureKKKKKKKKKKKKKKKKKK  5
    CHAPTER 2 LITERATURE REVIEWKKKKKKKKKKKKKK  6
    2.1 Previous Research Efforts on Discriminant AnalysisK  6
    2.2 The Features of DEA for Discriminant AnalysisKKK  9
    2.3 Misclassification Cost and RiskKKKKKKKKK  11
    CHAPTER 3A NOVEL PROCEDURE TO IDENTIFY THE MINIMIZED OVERLAP BOUNDARY OF TWO GROUPS BY DEA MODELKKKKKK  13
    3.1 MethodologyKKKKKKKKKKKKKKKKKKK  13
    3.1.1 Overlap IdentifyingKKKKKKKKKKKKKK  13
    3.1.2 Linear TransformationKKKKKKKKKKKKK  21
    3.2 ExampleKKKKKKKKKKKKKKKKKKKKK  25
    3.3 ConclusionKKKKKKKKKKKKKKKKKKKK  28
    CHAPTER 4A NOVEL PROCEDURE BASED ON DEA MODEL FOR THE TWO-GROUP DISCRIMINANT PROBLEMKKKKKKKKKKKKKKK 30
    4.1 MethodologyKKKKKKKKKKKKKKKKKKKK  31
    4.1.1 Establish Discriminant HyperplanesKKKKKKK  32
    4.1.2 Assign and Predict Group Membership for Observations34
    4.2 ExampleKKKKKKKKKKKKKKKKKKKKKK  36
    4.3 ConclusionKKKKKKKKKKKKKKKKKKKKK 43
    CHAPTER 5BANKRUPTCY PREDICTION USING ASYMMETRIC-STRATIFIED DEA MODELKKKKKKKKKKKKKKKKKKKKKKKK 45
    5.1 MethodologyKKKKKKKKKKKKKKKKKKKKK 48
    5.2 ApplicationKKKKKKKKKKKKKKKKKKKKK 51
    5.3 ResultKKKKKKKKKKKKKKKKKKKKKKK 53
    5.4 DiscussionKKKKKKKKKKKKKKKKKKKKK 58
    CHAPTER 6CONCLUSIONKKKKKKKKKKKKKKKKKK 61
    6.1 SummaryKKKKKKKKKKKKKKKKKKKKKK  61
    6.2 SuggestionsKKKKKKKKKKKKKKKKKKKK  62
    6.3 Study limitations and future researchesKKKKKK  63
    REFERENCEKKKKKKKKKKKKKKKKKKKKKKKK 65
    APPENDIXKKKKKKKKKKKKKKKKKKKKKKKKK77
    Reference Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance, 23, 589-609, 1968.
    Altman, E.I., Avery, R., Eisenbeis, R. and Stinkdy, J., Application of classification techniques in business, banking and finance. Contemporary Studies in Economic and Financial Analysis, 3, JAI Press, Greenwich, CT, 1981.
    Altman, E.I., Corporate financial distress - a complete guide to predicting, avoiding and dealing with bankruptcy. New York: Wiley, 1983.
    Altman, E.I., Corporate financial distress and bankruptcy. Second edition, John Wiley & Sons, New York, 1993.
    Altman, E.I., Marco, G.. and Varetto, F., Corporate distress diagnosis: comparisons using discriminant analysis and neural networks (the Italian experience). J Banking Finance, 18, 505-529, 1994.
    Banks, W.J. and Abad, P.L., On the performance of linear programming heuristics applied on a quadratic transformation in the classification problem. Eur J Opl Res, 74, 23-28, 1994.
    Bajgier, S.M. and Hill, A.V., An experimental comparison of statistical and linear programming approaches to the discriminant problem. Dec Sci, 13, 604-618, 1982.balacel, N., Multicriteria assignment method PROAFTN: Mehtodology and medical applications. Eur J Opl Res, 125, 175-183, 2000.
    Beaver, W.H., Financial ratios as predictors of failure. Empirical research in accounting: selected studies. Suppl J Acc Res, 5(4), 71-111, 1966.
    Berkson, J., Application of the logistic function to bio-assay. J Amer Stat Associ, 39, 357-365, 1944.
    Billings S.A. and Lee, K.L., Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm. Neural Networks, 15, 263-270, 2002.
    Catelani, M. and Fort, A., Fault diagnosis of electronic analog circuits using a radial basis function network classifier. Measurement, 28 (3), 147-158.
    Chang, D.S. and Kuo, Y.I., A novel procedure to identify the minimized overlap boundary of two groups by DEA model. Lecture Notes Comp Sci, 3483, 577-586, 2005.
    Charnes, A. and Cooper, W.W., Goal programming and multiple objective optimization. Eur J Opl Res, 1, 39-54, 1977.
    Charnes, A., Cooper, W.W and Rhodes, E., Measuring the efficiency of decision making units. Eur J Opl Res, 2, 429-444, 1978.
    Charnes, A., Data envelopment analysis: theory, methodology, and application, Kluwer Academic Publishers, Boston, 1994.
    Cielen, A., Peeters, L. and Vanhoof, K., Bankruptcy prediction using a data envelopment analysis. Eur J Opl Res, 154, 526-532, 2004.
    Cooper, W.W., Park, K.S. and Pastor, J.T., A range adjusted measure of inefficiency for use with additive models and relations to other models and measures in DEA. J Product Anal, 11, 5-42, 1999.
    Cooper, W.W., Seiford, L.M. and Tone, K., Data envelopment analysis: a comprehensive text with models, applications, references, and DEA-Solver software, Boston: Kluwer Academic Publishers, 2000.
    Courtis, J.K., Modelling a financial ratios categorical framework. J Bus Finan Acc, 5(4), 371-386, 1978.
    Deakin, B.E., A discriminant analysis of predictors of business failure. J Acc Res, Spring, 167-179, 1976.
    Diakoulaki, D., Zopounidis, C., Mavrotas, G. and Doumpos, M., The use of a preference disaggregation method in energy analysis and policy making. Energy, 24 (2), 157-166, 1999.
    Dimitras, A.I., Zopounidis, C. and Hurson, Ch., A multicriteria decision aid method for the assessment of business failure risk. Foundations of Comp Dec Sci, 20(2), 99-112, 1995.
    Dimitras, A.I., Zanakis, S.H. and Zopounidis, C., A survey of business failures with an emphasis on prediction methods and industrial applications. Eur J Opl Res, 90, 487-513, 1996.
    Doumpos, M. and Zopounidis, C., The use of the preference disaggregation analysis in the assessment of financial risks. Fuzzy Econ Rev, 3 (1), 39-57, 1999.
    Duarte Silva, A.P. and Stam, A., Second-order mathematical programming formulations for discriminant analysis. Eur J Opl Res, 74, 4-22, 1994.
    Duarte Silva, A.P. and Stam, A., A mixed-integer programming algorithm for minimizing the training sample misclassification cost in two-group classification. Annals Opl Res , 74, 129-157, 1997.
    Dutka, A., AMA handbook of customer satisfaction: a guide to research, Planning and Implementation. NTC Publishing Group, Illinois, 1195.
    Edminster, R.O., An empirical test of financial ratio analysis for small business failure prediction. J Finan Quant Anal, March, 1477-1498, 1972.
    Eisenbeis, R.A., Pitfalls in the application of discriminant analysis in business and economics. J Finance, 32, 875-900, 1977.
    Farrell, M.J., The measurement of productive efficiency. J R. Statist. Soc. Series A, 253-290, 1957.
    Fisher, R.A., The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179-188, 1936.
    Flinkman, M., Michalowski, W., Nilsson, S., Slowinski, R., Susmaga, R. and Wilk, D., Use of rough sets analysis to classify Siberian forest ecosystem according to net primary production of phytomass. INFOR, 38 (3), 145-161, 2000.
    Frankel, J.A. and Rose, A.K., Currency crashes in emerging markets: an empirical treatment. J Int Econ., 41, 351-366, 1996.
    Freed, N. and Glover, F., A linear programming approach to the discriminant problem. Dec Sci, 12, 68-74, 1981a.
    Freed, N. and Glover, F., Simple but powerful goal programming models for discriminant problems. Eur J Opl Res, 7, 44-60, 1981b.
    Freed, N. and Glover, F., Evaluating alternative linear programming models to solve the two-group discriminant problem. Dec Sci, 17, 151-162, 1986.
    Frydman, H., Atlman, E.I. and Kao, D.L., Introducing recursive partitioning for financial classification: the case of financial distress. J Finance, 40, 269-291, 1985.
    Gehrlein, W.V. and Wagner, B.J., A two-stage least cost credit scoring model. Annals Opl Res, 74, 159-171, 1997.
    Glover, F., Keene, S. and Duea, B., A new class of models for the discriminant problem. Dec Sci, 19, 269-280, 1988.
    Gochet, W., Stam, A., Srinivasan, V. and Chen, S., Multigroup discriminant analysis using linear programming. Opl Res, 45 (2), 213-225, 1997.
    Greco., Matarazzo, B. and Slowinski, R., A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis, C. (Ed.), operational Tools in the Management of Financial Risks. Kouwer Academic Publishers, Dordrecht, 121-136, 1998.
    Grice, J.S. and Ingram, R.W., Tests of the generalization of Altmans bankruptcy prediction model. J Bus Res, 54, 53-61, 2001.
    Gupta, Y.P., Rao, R.P. and Bagghi, P.K., Linear goal programming as an alternative to multivariate discriminant analysis: a note. J Bus Finan Acc, 17(4), 593-598, 1990.
    Hull, J., Financial risk management. Course Notes, University of Toronto, MGT2315 Winter, 1998, 1998.
    International Monetary Fund, Annual Report 1997/1998, The IMF, 1998.
    International Monetary Fund, World Economic Outlook, The IMF, 1998.
    Joachimsthaler, W.A. and Stam, A., Four approaches to the classification problem in discriminant analysis: An experimental study. Dec Sci, 19: 322-333, 1988.
    Johnson, R.A., Wichern, D.W., Applied Multivariate Statistical Analysis. 3rd edn. Pretice-Hall, Englewood Cliffs New Jersey, 519-521, 1992.
    Koehler, G.J. and Erenguc, S.S., Minimizing misclassifications in linear discriminant analysis. Dec Sci, 21, 63-85, 1990.
    Lacher, R.C., Coats, P.K., Sharma, S.C. and Fant, L.F., A neural network for classifying the financial health of a firm. Eur J Opl Res, 85, 53-65, 1995.
    Lam, K.F. and Choo, E.U., A linear goal programming model for classification with non-monotonic attributes. J Comp Opns Res, 20, 403-408, 1993.
    Lam, K.F., Choo, E.U. and Wedley, W.C., Linear goal programming in estimation of classification probability. Eur J Opl Res, 67, 101-110, 1993.
    Lam, K.F. and Moy, J.W., An experimental comparison of some recently developed linear programming approaches to the discriminant problem. J Comp Opns Res, 24, 593-599, 1997.
    Lam, K.F. and Moy, J.W., A piecewise linear programming approach to the two-roup discriminant problemVan adaptation to Fishers linear discriminant function model. Eur J Opl Res, 145, 471-481, 2003.
    Lam, K.F., Choo, E.U. and Moy, J.W., Minimizing deviations from the group mean: A new linear programming approach for the two-group classification problem. Eur J Opl Res, 88, 358-367, 1996.
    Lawrence, S., Giles, C.L. and Tsoi, A.C., Lessons in neural network training: overfitting may be harder than expected. In Proceedings of the fourteenth national conference on artificial intelligence, Mento Park, 540-554, 1997.
    Luoma, M. and Laitinen, E.K., Survival analysis as a tool for company failure prediction. Omega, 19(6), 673-378, 1991.
    McFadden, D., Conditional logit analysisi in qualitative choice behavior. In: Zarembka, P. (Ed.), Frontiers in Econometrics. Academic Press, New York, 1974.
    Messier, W.F. and Hansen, J.V., Including rules for expert system development: an example using default and bankruptcy data. Manage Sci, 34(12), 1403-1415, 1988.
    Michalowski, W., Rubin, S., Slowinski, R. and Wilk, S., Triage of the child with abdominal pain: A clinical algorithm for emergency patient management. Paediatr Child Heal, 6 (1), 23-28, 2001.
    Miyakoshi, T., The causes of the Asian currency crisis: empirical observations. Japan World Economy, 12, 243-253, 2000.
    Moody, J.E., The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. NIPS, 4, 847-854, 1992.
    Nieddu, L. and Patrizi, G., Formal methods in pattern recognition: A review. Eur J Opl Res, 120, 459-495, 2000.
    Nowicki, R., Slowinski, R. and Stefanowski, J., Evaluation of vibroacoustic diagnostic symptoms by means of the rough sets theory. J Comput Ind, 20, 141-152, 1992.
    Ohlson, J.A., Financial ratios and the probabilistic prediction of bankruptcy. J Acc Res, 18(1), 109-131, 1980.
    Paradi, J.C., Asmild, M. and Simak, P.C., Using DEA and worst practice DEA in credit risk evaluation. J. Productiv Anal, 21, 153-165, 2004.
    Pendharkar, P.C., A potential use of data envelopment analysis for the inverse classification problem. Omega 30, 243-248, 2002.
    Retzlaff-Roberts, D. and Puelz, R., Classification in automobile insurance using a DEA and discriminant analysis hybrid, J Produtiv Anal, 7, 417-427, 1996.
    Ripley, B.D., pattern recognition and neural networks. Cambridge University Press, Cambridge, 1996.
    Rossi, L., Slowinski., R. and Susmaga, R., Rough set approach to evaluation of stormwater pollution. I J Environ and Pollu, 12 (2-3), 232-250, 1999.
    Rubin, P.A., A comment regarding polynomial discriminant functions. Eur J Opl Res, 74, 29-31, 1994.
    Rulon, PlJ., Tiedeman, D.V., Tatsuoka, M.M. and Langmuir, C.R., Multivariate statistics for personnel classification. Wiley, New York., 1967.
    Seiford, L.M. and Zhu, J., An acceptance system decision rule with data envelopment analysis. Comp Opns Res, 25(4), 329-332, 1996.
    Sharda, R. and Wilson, R.L., 1996. Neural network experiments in business-failure forecasting: predictive performance measurement issues. Int J Comp Intel Organ, 1(2), 107-117, 1998.
    Shen, L., Tay, F.E.H., Qu, L. and Shen, Y., Fault diagnosis using rough sets theory. Comput Ind, 43, 61-72, 2000.
    Siskos, Y., Grigoroudis, E., Zopounidis, C. and Saurais, O., Measuring customer satisfaction using a survey based preference disaggregation model. J Global Optim, 12 (2), 175-195, 1998.
    Slowinski, R. and Zopounidis, C., Application of the rough set approach to evaluation of bankruptcy risk. I J Intell Syst Acc Finan Manage, 4 (1), 27-41, 1995.
    Smith, C., Some examples of discrimination. Ann Eugen, 13, 272-282, 1947.
    Smith, M., Neural networks for statistical modeling. New York: Van Nostrand Reinhold, 1993.
    Sowlati, T., Paradi, J.C. and Suld, C., Information systems project prioritization using data envelopment analysis. Math Comp Modelling, 41(11-12), 1279-1298, 2005
    Stam, A., Extensions of mathematical programming based classification rules: A multicriteria approach. Eur J Opl Res, 48, 351-361, 1990.
    Stefanowski, J. and Slowinski, R., Rough set theory and rule induction techniques for discovery of attribute dependencies in medical information systems. Bulletin of th Plish Academy of Sciences, ser. Tech Sci, 46 (2), 247-263, 1998.
    Sueyoshi, T., DEA-discriminant analysis in the view of goal programming. Eur J Opl Res, 115, 564-582, 1999.
    Sueyoshi, T., Extended DEA-discriminant analysis. Eur J Opl Res, 131, 324-351, 2001.
    Sueyoshi, T., Mixed integer programming approach of extended DEA- discriminant analysis. Eur J Opl Res, 152, 45-55, 2004.
    Sueyoshi, T., DEA-Discriminant analysis: methodological comparison among eight discriminant analysis approaches. Eur J Opl Res, 169, 147-272, 2006.
    Tam, K.Y. and Kiang, M.Y., Managerial applications of neural networks: The case of bank failure predictions. Manag Sci, 38(7), 926-947, 1992.
    Troutt, M.D., Rai, A. and Zhang, A., The potential use of DEA for credit applicant acceptance systems. Comp Opns Res, 23(4), 405-408, 1996.
    Tsumoto, S., Automated extraction of medical expert system rules from clinical databases based on rough set theory. Inform Sciences, 112, 67-84, 1998.
    Ward, T., Using information from the statement of cash flows to predict insolvency. J Commer Lending, 77(7), 29-34, 1995.
    Weigend, A., On overfitting and the effective number of hidden units. In Proceeding of the 1993 connectionist models summer school, 335-342, 1994.
    Wilson, R.L. and Sharda, R., Bankruptcy prediction using neural networks. Dec Support Syst, 11, 545-557, 1994.
    World Bank, Global Development Finance: Country Tables 1999, The World Bank, 1999.
    Zhu, J., Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets and DEA Excel solver, Kluwer Academic Publishers, Boston, 2003.
    Zmijewski, M.E., Methodological issues related to the estimation of financial distress prediction models. J Ac Re, 22, 59-82, 1984.
    Zopounidis, C., Pardalos, P.M., Doumpos, M. and Mavridou, Th., Multicriteria decision aid in credit cards assessment. In: Zopounidis, C., Pardalos, P.M. (Eds), Managing in Uncertainty: Theory and Practice. Kluwer Academic Publishers, Dordrecht, 163-178, 1998.
    Zopounidis, C., Doumpos, M. and Zanakis, S.H., Stock evaluation using a preference disaggregation methodology. Dec Sci, 30 (2), 313-336, 1999a.
    Zopounidis, C., Slowinski, R., Doumpos, M., Dimitras, A.I. and Susmaga, R., Business failure prediction using rough sets: A comparision with multivariate analysis techniques. Fuzzy Econ Rev, 4 (1), 3-33, 1999b.
    Advisor
  • Dong-shang Chang(iF)
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    Date of Submission 2007-07-11

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