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Statistical Classification Methods and Their Application in Credit Scoring
dc.creatorKomorád, Karel
dc.date.accessioned2017-03-17T11:29:21Z
dc.date.available2017-03-17T11:29:21Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/20.500.11956/3196
dc.description.abstractIn our thesis we carry out an empirical data set analysis and a thorough case study of statistical classi cation techniques in credit scoring. For our data set the logistic regression model appears to be the most suitable classi cation method in comparison with classi cation trees and knearest neighbours method. Moreover, only the logistic regression allows us to use similarity measures for comparison of classi ers. Further we show that the usage of standardized costs is inappropriate in the case of credit scoring and might lead to acceptance of all applicants for a credit. We also gure out that for strongly unbalanced data the classi cation trees might be lacking in discrimination power.en_US
dc.languageČeštinacs_CZ
dc.language.isocs_CZ
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.titleStatistické metody klasifikace a jejich využití pro kreditní skórovánícs_CZ
dc.typerigorózní prácecs_CZ
dcterms.created2006
dcterms.dateAccepted2006-01-31
dc.description.departmentDepartment of Probability and Mathematical Statisticsen_US
dc.description.departmentKatedra pravděpodobnosti a matematické statistikycs_CZ
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.identifier.repId43921
dc.title.translatedStatistical Classification Methods and Their Application in Credit Scoringen_US
dc.identifier.aleph001448372
thesis.degree.nameRNDr.
thesis.degree.levelrigorózní řízenícs_CZ
thesis.degree.disciplineProbability, mathematical statistics and econometricsen_US
thesis.degree.disciplinePravděpodobnost, matematická statistika a ekonometriecs_CZ
thesis.degree.programMathematicsen_US
thesis.degree.programMatematikacs_CZ
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csPravděpodobnost, matematická statistika a ekonometriecs_CZ
uk.degree-discipline.enProbability, mathematical statistics and econometricsen_US
uk.degree-program.csMatematikacs_CZ
uk.degree-program.enMathematicsen_US
thesis.grade.csProspělcs_CZ
thesis.grade.enPassen_US
uk.abstract.enIn our thesis we carry out an empirical data set analysis and a thorough case study of statistical classi cation techniques in credit scoring. For our data set the logistic regression model appears to be the most suitable classi cation method in comparison with classi cation trees and knearest neighbours method. Moreover, only the logistic regression allows us to use similarity measures for comparison of classi ers. Further we show that the usage of standardized costs is inappropriate in the case of credit scoring and might lead to acceptance of all applicants for a credit. We also gure out that for strongly unbalanced data the classi cation trees might be lacking in discrimination power.en_US
uk.publication-placePrahacs_CZ
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra pravděpodobnosti a matematické statistikycs_CZ


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