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dc.contributor Graduate Program in Systems and Control Engineering.
dc.contributor.advisor Şıkoğlu, Tamer.
dc.contributor.author Akar, Serdar.
dc.date.accessioned 2023-03-16T11:34:43Z
dc.date.available 2023-03-16T11:34:43Z
dc.date.issued 2007.
dc.identifier.other SCO 2007 A33
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/15630
dc.description.abstract One of the most important innovative concepts is the credit scoring. Today it can be interested in different sectors. Thus the improvement of credit scoring is increasing day by day. The credit scoring with the help of classification techniques provides to take easy and quick decisions in lending. However, no definite consensus has been reached with regard to the best method for credit scoring and in what conditions the meth- ods performs best. Although a huge range of classification techniques has been used in this area, the logistic regression has been seen an important tool and used very widely in studies. This study aims to examine accuracy and bias properties in parameter estimation of the logistic regression (binary logistic) , linear discriminant analysis , linear regression by using German Data which has different variables, data types, real basement and accurately results. Moreover, application of these significant statistical analyzes on German data is provided and the method accuracies are examine for new consumer elements by the software application. Finally, ratings on the results of best method is done by hybrid model by its most reliance comparing and completion all methods.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007.
dc.subject.lcsh Credit scoring systems.
dc.title Hybrid model of credit scoring
dc.format.pages xiii, 70 leaves;


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