A Comparative Study of Credit Scorecard Modeling and its Discriminatory Power Using Concordance Probability

dc.contributor.guide	Kiruthika
dc.coverage.spatial
dc.creator.researcher	Dilsha M
dc.date.accessioned	2018-09-17T05:54:37Z
dc.date.available	2018-09-17T05:54:37Z
dc.date.awarded
dc.date.completed	2015
dc.date.registered	29/11/2010
dc.description.abstract	newline
dc.description.note
dc.format.accompanyingmaterial	DVD
dc.format.dimensions
dc.format.extent	x, 133p.
dc.identifier.uri	http://hdl.handle.net/10603/215189
dc.language	English
dc.publisher.institution	Department of Statistics
dc.publisher.place	Pondicherry
dc.publisher.university	Pondicherry University
dc.relation
dc.rights	university
dc.source.university	University
dc.subject.keyword	Artificial Neural Network
dc.subject.keyword	Concordance Probability
dc.subject.keyword	Credit Scorecard
dc.subject.keyword	Predictive Clustering
dc.subject.keyword	Support Vector Machine
dc.title	A Comparative Study of Credit Scorecard Modeling and its Discriminatory Power Using Concordance Probability
dc.title.alternative
dc.type.degree	Ph.D.