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dc.creatorPrieto Orlando, Rodrigo Javier
dc.date.accessioned2012-06-07T22:38:03Z
dc.date.available2012-06-07T22:38:03Z
dc.date.created1994
dc.date.issued1994
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-1994-THESIS-P949
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.en
dc.descriptionIncludes bibliographical references.en
dc.description.abstractThis thesis describes a theoretical analysis and a series of empirical tests of a pattern recognition based Early Warning System for bank failure prediction. The theoretical analysis centers on the binarization, feature selection and feature construction procedures. Alternative methods for the selection of binarization cutpoints are suggested and tested. Finally, empirical comparisons of the prediction accuracy of the Early Warning System against those obtained with Multiple Discriminant Analysis and logit models are described and analyzed. This research shows that the pattern recognition Early Warning System is a powerful, flexible and accurate prediction and classification system.en
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherTexas A&M University
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.subjectindustrial engineering.en
dc.subjectMajor industrial engineering.en
dc.titleA theoretical analysis of a pattern recognition algorithm for bank failure predictionen
dc.typeThesisen
thesis.degree.disciplineindustrial engineeringen
thesis.degree.nameM.S.en
thesis.degree.levelMastersen
dc.type.genrethesisen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen


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