Analysis of international debt problem using artificial neural networks and statistical methods

• Published in: Neural computing & applications Vol. 19; no. 8; pp. 1207 - 1216
• Main Authors: Yazici, Berna, Memmedli, Memmedaga, Aslanargun, Atilla, Asma, Senay
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.11.2010; Springer
• Subjects: 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

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...