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http://dx.doi.org/10.5351/KJAS.2012.25.2.261

Cutpoint Selection via Penalization in Credit Scoring  

Jin, Seul-Ki (Department of Statistics, University of Seoul)
Kim, Kwang-Rae (Institute of Statistics, Korea University)
Park, Chang-Yi (Department of Statistics, University of Seoul)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.2, 2012 , pp. 261-267 More about this Journal
Abstract
In constructing a credit scorecard, each characteristic variable is divided into a few attributes; subsequently, weights are assigned to those attributes in a process called coarse classification. While partitioning a characteristic variable into attributes, one should determine appropriate cutpoints for the partition. In this paper, we propose a cutpoint selection method via penalization. In addition, we compare the performances of the proposed method with classification spline machine (Koo et al., 2009) on both simulated and real credit data.
Keywords
Classification spline machine; coarse classification; credit scorecard;
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