BibTex RIS Kaynak Göster

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Yıl 2015, Cilt: 29 Sayı: 4, 0 - , 21.10.2015

Öz

The classification done by Human Development Index comes into prominence through countries, which take into consideration the level of development instead of economic growth. The United Nations Development Programme has been classifying countries by using this index since 1990. The aims of this study are to determine the importance of the variables used for preparing Human Development Index and by developing the discriminant function, providing classification with fewer variables for the future. In this analysis, the classification of the Human Development Index by United Nations Development Programme (UNDP) is examined and necessary transformations for ensuring a discriminant analysis of the examined data are made. The obtained variables are then used in a discriminant analysis. One discriminant function is constructed since only very developed and mid-developed countrygroups are analyzed. As a result, a function with a high classification success of 92.5% is obtained. Interpretation of the coefficients of variables involved in the function and the effect of variables on classification have been analyzed

Kaynakça

  • Balcaen, S., H. Ooghe. 35 Years of Studies on Business Failure: An Overview of the Classic Statistical Methodologies and Their Related Problems. The British Accounting Review. 2006. Vol.38. p. 63-93.
  • Berg D. Bankruptcy Prediction by Generalized Additive Models. Applied Stochastic Models in Business and Industry. 2004. Vol.23. p. 129- 143.
  • Bhanoji, Rao. Human Development Report: 1990 Review Assessment. World Development. 1991. Vol.19. p.10.
  • Bosse, D. A. Bundling Governance Mechanisms to Efficiently Organize Small Firm Loans. Journal of Business Venturing. 2008. Vol.24. p. 183-195.
  • Chen, K., D.C. Yen, S. Hung, A.H. Huang. An Exploratory Study of the Selection of Communication Media: The Relationship between Flow and Communication Outcomes. Decision Support Systems. 2008. Vol.45. p. 822-832.
  • Cheng, B., D.M. Titterington. Neural Networks: A Review from a Statistical Perspective. Statistical Science, 1994. Vol. 9(1). p. 2-30.
  • Çilan, Ç.A., B.A. Bolat, E. Coşkun. Analyzing Digital Divide Within and Between Member and Candidate Countries of European Union, Government Information Quarterly, 2009. Vol.26. p.98-105.
  • Gaspar, Des. Kalkınma Ahlakı Yeni Bir Alan mı? Piyasa Güçleri ve Küresel Kalkınma. İngilizceden Çeviren: İdil Eser. Yapı Kredi Publications. 1995. No.2. İstanbul.
  • Klecka, W. Discriminant Analysis. 1980. Sage Publications. London.
  • Lachenbruch, P.A. Discriminant Analysis. 1975. Hafner Press. London.
  • Lee, K., D. Booth, P. Alam. Comparison of Supervised and Unsupervised Neural Networks in Predicting Bankruptcy of Korean Firms. Expert Systems with Applications. 2005. Vol.29 (1). p. 1-16.
  • Leshno, M., Y. Spector. Neural Network Prediction Analysis: The Bankruptcy Case. Neurocomputing. 1996. Vol.10. p. 125-147.
  • Liang Z., P. Shi. Kernel Discriminant Analysis and Its Theoretical Foundation. The Journal of the Pattern Recognition Society. 2004. Vol.38, p.445- 447.
  • Lu, J., K.N. Plataniotis, A.N. Venetsanapoulos, J. Wang. An Efficient Kernel Discriminant Analysis Method. The Journal of the Pattern Recognition Society. 2005. Vol.38. p. 1788-1790.
  • Malhotra, R., D.K. Malhotra. Evaluating Consumer Loans Using Neural Networks. The International Journal of Management Sciences. 2003. Vol.31. p. 83-96.
  • Odom, M., R. Sharda. A Neural Network Model for Bankruptcy Prediction. Proceedings of the International Joint Conference on Neural Networks. 1990. Vol.2. p. 163-168.
  • Pompe, P.P.M., J. Bilderbeek. The Prediction of Bankruptcy of Small-and- Medium Sized Industrial Firms. Journal of Business Venturing. 2005. Vol.20. p. 847-868.
  • Sharma S. . Applied Multivariate Techniques. 1996. John Wiley and Sons Inc. Canada.
  • Srivastava S., M. Gupta, B. Frigyik. Bayesian Quadratic Discriminant Analysis. Journal of Machine Learning Research. 2007. Vol.8. p. 1277-1305.
  • Sueyoshi, T. A Methodological Comparison between Standard and Two Stage Mixed Integer Approaches for Discriminant Analysis. Asia-Pacific Journal of Operations Research. 2004. Vol.4. p. 513-528.
  • Tang H., T. Fang, P. Shi. Laplacian Discriminant Analysis. The Journal of the Pattern Recognition Society. 2005. Vol.39. p. 136-139.
  • UNDP (1990). Human Development Report 1990. Oxford University Press. New York.
  • UNDP (1991). Human Development Report 1991. Oxford University Press. New York.
  • UNDP (1994). Human Development Report 1994. Oxford University Press. New York.
  • UNDP (1996). Human Development Report 1996. Oxford University Press. New York.
  • UNDP (2004). Human Development Report 2004. Oxford University Press. New York.
  • UNDP (2005). Human Development Report 2005. Oxford University Press. New York.
  • UNDP (2006). Human Development Report 2006. Oxford University Press. New York.
  • UNDP (2008). Human Development Report 2007/2008. Oxford University Press. New York.
  • Wu D.D., L. Liang, Y. Zijiang. Analyzing Financial Distress of Chinese Public Companies Using Probabilistic Neural Networks and Multivariate Discriminate Analysis. Socio Economic Planning Sciences. 2008. Vol.42. p. 206-220.
  • Zheng, W. A Note on Kernel Uncorrelated Discriminant Analysis. The Journal of the Pattern Recognition Society. 2005. Vol.38. p. 2185-2187.

A STATISTICAL CLASSIFICATION STUDY OF COUNTRIES’ HUMAN DEVELOPMENT LEVEL BY DISCRIMINANT ANALYSIS

Yıl 2015, Cilt: 29 Sayı: 4, 0 - , 21.10.2015

Öz

The classification done by Human Development Index comes into prominence through countries, which take into consideration the level of development instead of economic growth. The United Nations Development Programme has been classifying countries by using this index since 1990. The aims of this study are to determine the importance of the variables used for preparing Human Development Index and by developing the discriminant function, providing classification with fewer variables for the future. In this analysis, the classification of the Human Development Index by United Nations Development Programme (UNDP) is examined and necessary transformations for ensuring a discriminant analysis of the examined data are made. The obtained variables are then used in a discriminant analysis. One discriminant function is constructed since only very developed and mid-developed country-groups are analyzed. As a result, a function with a high classification success of 92.5% is obtained. Interpretation of the coefficients of variables involved in the function and the effect of variables on classification have been analyzed.

Kaynakça

  • Balcaen, S., H. Ooghe. 35 Years of Studies on Business Failure: An Overview of the Classic Statistical Methodologies and Their Related Problems. The British Accounting Review. 2006. Vol.38. p. 63-93.
  • Berg D. Bankruptcy Prediction by Generalized Additive Models. Applied Stochastic Models in Business and Industry. 2004. Vol.23. p. 129- 143.
  • Bhanoji, Rao. Human Development Report: 1990 Review Assessment. World Development. 1991. Vol.19. p.10.
  • Bosse, D. A. Bundling Governance Mechanisms to Efficiently Organize Small Firm Loans. Journal of Business Venturing. 2008. Vol.24. p. 183-195.
  • Chen, K., D.C. Yen, S. Hung, A.H. Huang. An Exploratory Study of the Selection of Communication Media: The Relationship between Flow and Communication Outcomes. Decision Support Systems. 2008. Vol.45. p. 822-832.
  • Cheng, B., D.M. Titterington. Neural Networks: A Review from a Statistical Perspective. Statistical Science, 1994. Vol. 9(1). p. 2-30.
  • Çilan, Ç.A., B.A. Bolat, E. Coşkun. Analyzing Digital Divide Within and Between Member and Candidate Countries of European Union, Government Information Quarterly, 2009. Vol.26. p.98-105.
  • Gaspar, Des. Kalkınma Ahlakı Yeni Bir Alan mı? Piyasa Güçleri ve Küresel Kalkınma. İngilizceden Çeviren: İdil Eser. Yapı Kredi Publications. 1995. No.2. İstanbul.
  • Klecka, W. Discriminant Analysis. 1980. Sage Publications. London.
  • Lachenbruch, P.A. Discriminant Analysis. 1975. Hafner Press. London.
  • Lee, K., D. Booth, P. Alam. Comparison of Supervised and Unsupervised Neural Networks in Predicting Bankruptcy of Korean Firms. Expert Systems with Applications. 2005. Vol.29 (1). p. 1-16.
  • Leshno, M., Y. Spector. Neural Network Prediction Analysis: The Bankruptcy Case. Neurocomputing. 1996. Vol.10. p. 125-147.
  • Liang Z., P. Shi. Kernel Discriminant Analysis and Its Theoretical Foundation. The Journal of the Pattern Recognition Society. 2004. Vol.38, p.445- 447.
  • Lu, J., K.N. Plataniotis, A.N. Venetsanapoulos, J. Wang. An Efficient Kernel Discriminant Analysis Method. The Journal of the Pattern Recognition Society. 2005. Vol.38. p. 1788-1790.
  • Malhotra, R., D.K. Malhotra. Evaluating Consumer Loans Using Neural Networks. The International Journal of Management Sciences. 2003. Vol.31. p. 83-96.
  • Odom, M., R. Sharda. A Neural Network Model for Bankruptcy Prediction. Proceedings of the International Joint Conference on Neural Networks. 1990. Vol.2. p. 163-168.
  • Pompe, P.P.M., J. Bilderbeek. The Prediction of Bankruptcy of Small-and- Medium Sized Industrial Firms. Journal of Business Venturing. 2005. Vol.20. p. 847-868.
  • Sharma S. . Applied Multivariate Techniques. 1996. John Wiley and Sons Inc. Canada.
  • Srivastava S., M. Gupta, B. Frigyik. Bayesian Quadratic Discriminant Analysis. Journal of Machine Learning Research. 2007. Vol.8. p. 1277-1305.
  • Sueyoshi, T. A Methodological Comparison between Standard and Two Stage Mixed Integer Approaches for Discriminant Analysis. Asia-Pacific Journal of Operations Research. 2004. Vol.4. p. 513-528.
  • Tang H., T. Fang, P. Shi. Laplacian Discriminant Analysis. The Journal of the Pattern Recognition Society. 2005. Vol.39. p. 136-139.
  • UNDP (1990). Human Development Report 1990. Oxford University Press. New York.
  • UNDP (1991). Human Development Report 1991. Oxford University Press. New York.
  • UNDP (1994). Human Development Report 1994. Oxford University Press. New York.
  • UNDP (1996). Human Development Report 1996. Oxford University Press. New York.
  • UNDP (2004). Human Development Report 2004. Oxford University Press. New York.
  • UNDP (2005). Human Development Report 2005. Oxford University Press. New York.
  • UNDP (2006). Human Development Report 2006. Oxford University Press. New York.
  • UNDP (2008). Human Development Report 2007/2008. Oxford University Press. New York.
  • Wu D.D., L. Liang, Y. Zijiang. Analyzing Financial Distress of Chinese Public Companies Using Probabilistic Neural Networks and Multivariate Discriminate Analysis. Socio Economic Planning Sciences. 2008. Vol.42. p. 206-220.
  • Zheng, W. A Note on Kernel Uncorrelated Discriminant Analysis. The Journal of the Pattern Recognition Society. 2005. Vol.38. p. 2185-2187.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Serhat Burmaoğlu

Erkan Oktay

Yayımlanma Tarihi 21 Ekim 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 29 Sayı: 4

Kaynak Göster

APA Burmaoğlu, S., & Oktay, E. (2015). A STATISTICAL CLASSIFICATION STUDY OF COUNTRIES’ HUMAN DEVELOPMENT LEVEL BY DISCRIMINANT ANALYSIS. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 29(4).

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