A Study of Reject Inference Techniques

dc.contributor.authorAnderson, Billie
dc.date.accessioned2023-07-05T20:18:33Z
dc.date.available2023-07-05T20:18:33Z
dc.date.issued2008
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractCredit Scorecard models are commonly built using data available within anorganization's transactional database. Such data, however, will only contain informationfor those applicants who were "accepted" or previously awarded credit by theorganization; data will not be available for those applicants who were "rejected." Theuse of reject inference to adjust credit scorecard models for the missing data representedby rejected loan applications is common practice, and several approaches are used intoday's financial industry.The use of mixture models presents an alternative approach for reject inference.In this approach, the underlying probability density function (PDF) for the totalpopulation of all applicants is modeled as a weighted average of the PDFs of the acceptedpopulation and the rejected population. The unknown parameters in this model will thenbe the mixing parameters of this weighted average. To estimate these parameters, we usethe Expectation-Maximization (EM) algorithm wherein the data associated with therejected applicants is treated as missing completely at random. Simulated data derivedfrom actual case studies are used to assess the effectiveness of the mixture modelapproach and other reject inference techniques.Next, an alternative reject inference model is developed using a logistic regressionapproach. The parameters of this alternative model are derived using the EM algorithm.A simulation study will be performed to determine if the alternative reject inferencemodel offers any advantages over the financial industry's current standard rejectinference approach.Finally, a case study using data from a real-world reject inference data set will beconducted to determine how well the newly developed reject inference technique usingthe EM algorithm performs when compared to the standard method of reject inference.en_US
dc.format.extent132 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://ir.ua.edu/handle/123456789/10161
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.
dc.subjectReject inference
dc.subject.lcshCredit scoring systems
dc.subject.lcshLogistic regression analysis
dc.subject.lcshExpectation-maximization algorithms
dc.titleA Study of Reject Inference Techniquesen_US
dc.typethesis/dissertation
dc.typetext
etdms.degree.departmentUniversity of Alabama, Department of Information Systems, Statistics, and Management Science
etdms.degree.disciplineBusiness
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.
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