Analysis of international debt problem using artificial neural networks and statistical methods
It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models shou...
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| Veröffentlicht in: | Neural computing & applications Jg. 19; H. 8; S. 1207 - 1216 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.11.2010
Springer |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries’ international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture. |
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| AbstractList | It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries’ international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture. |
| Author | Asma, Senay Yazici, Berna Aslanargun, Atilla Memmedli, Memmedaga |
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| Cites_doi | 10.1016/S0893-6080(05)80056-5 10.1111/j.1747-7093.2003.tb00432.x 10.1080/02664769921927 10.1016/S0169-2070(97)00044-7 10.1016/j.eswa.2003.12.009 10.1016/S0893-6080(03)00170-9 10.1007/b98874 10.1080/10629360600564874 10.1017/S0020818300019123 10.2307/1885958 10.1016/j.eswa.2005.04.034 10.1017/S0020818300019147 10.1016/S0893-6080(96)00102-5 10.1016/0304-3878(77)90004-9 10.1007/BF01501506 10.1214/ss/1177010638 10.1080/07474939408800273 10.1016/S0893-6080(98)00116-6 10.1016/0022-1996(71)90004-3 10.1016/S0305-0548(99)00144-6 10.1287/inte.24.2.116 10.1093/oso/9780198538493.001.0001 10.2307/3866591 10.1111/j.1468-0394.1995.tb00114.x 10.1109/ICNN.1993.298623 |
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| Keywords | Conjugate gradient method Logistic and probit regression Backpropagation algorithm Quasi-Newton method Artificial neural network Rescheduling and non-rescheduling of the international debts Backpropagation Evaluation Neural computation Statistical analysis Prediction Regression analysis Neural network Economic sciences Neural net architecture Statistical method Statistical regression Logistic regression Analysis method Classification Multilayer network |
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| Title | Analysis of international debt problem using artificial neural networks and statistical methods |
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