Reject inference, augmentation, and sample selection
نویسندگان
چکیده
منابع مشابه
Reject inference, augmentation, and sample selection
Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Alternatively, practitioners often see the problem as one of missing data where the relationship in the new model is biased because the behaviour of the omitted cases differs from that of those who make up the sample for...
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متن کاملExplorer Reject inference in survival analysis by augmentation
General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edin...
متن کاملLean Models and Reject Inference
Credit scoring models are normally built using only applicants who have been previously accepted for credit. Such non-random sample selection may produce bias in estimated model parameters and accordingly model predictions of repayment performance may not be optimal. Previous empirical research suggests that omission of rejected applicants has a detrimental impact on model estimation and predic...
متن کاملReject Inference and Survival Modelling
The literature suggests that the commonly used augmentation method of reject inference does little to alter the ranking performance of application scorecards, but is does improve the ability to detect an appropriate cutoff score. In this paper we examine whether the use of augmentation will improve the ranking of applicants in terms of default for various time frames, using survival analysis. T...
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ژورنال
عنوان ژورنال: European Journal of Operational Research
سال: 2007
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2006.06.072