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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Vydané v:Neural computing & applications Ročník 19; číslo 8; s. 1207 - 1216
Hlavní autori: Yazici, Berna, Memmedli, Memmedaga, Aslanargun, Atilla, Asma, Senay
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer-Verlag 01.11.2010
Springer
Predmet:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Inference from stochastic processes; time series analysis
Learning and adaptive systems
Linear inference, regression
Mathematics
Multivariate analysis
Original Article
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
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
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-010-0422-4