Master of Science (MS)
First Committee Member
Number of Pages
Statistical models are most predictive and robust when the historical data used to build the model covers sufficient representation from the population. A financial organization uses a scoring model, developed with historical data, to determine if a consumer should be granted a line of credit for which they apply. When the existing model is too old or the applicant population has changed, a new model is to be developed, but the range of historical data, essential to the predictive model development, does not include the entire applicant population because of the declines. A technique called reject inference is typically used to infer the unknown performance of the reject population as a new scoring model is developed. This paper explores several techniques that have been published, selects the most promising technique, and introduces an adjustment to the existing technique.
Data; Inference; Methodology; Missing; Problems; Reject; Skewed
University of Nevada, Las Vegas
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Watanabe, Atsuko, "Reject inference methodology skewed missing data problem" (2005). UNLV Retrospective Theses & Dissertations. 1810.
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