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Optimal thresholding for binary classification applied in credit scoring

dc.contributor.authorRossa, Agnieszka
dc.date.accessioned2026-07-10T08:25:32Z
dc.date.available2026-07-10T08:25:32Z
dc.date.issued2026-06-30
dc.identifier.issn2391-6478
dc.identifier.urihttp://hdl.handle.net/11089/58857
dc.description.abstractThe paper concerns a new method of classifying individuals into two subpopulations and demonstrates the application of this method in credit scoring. Individuals are classified into two subpopulations depending on the duration   of a certain phenomenon (e.g., default). The duration may be shorter or longer than a certain fixed value . It is assumed that the variable  is not known at the time of classification, so the explanatory continuous predictive marker is used instead. The optimal acceptance threshold for a predictive marker is determined by a time-dependent receiver operating curve (ROC) estimated from a random sample. A typical complexity of time-to-event data is that observations in the sample can be right-censored. Therefore, the estimation is based on a sequential random sampling and the Kaplan-Meier estimator.en
dc.description.abstractArtykuł dotyczy nowej metody klasyfikacji jednostek na dwie subpopulacje i przedstawia zastosowanie tej metody w ocenie zdolności kredytowej. Osoby są klasyfikowane do dwóch subpopulacji w zależności od czasu trwania pewnego zjawiska (np. niewypłacalności). Czas trwania może być krótszy lub dłuższy niż określona stała wartość t. Zakłada się, że taka zmienna nie jest znana w momencie klasyfikacji, dlatego zamiast niej stosuje się ciągły marker predykcyjny. Optymalny próg akceptacji dla markera predykcyjnego określa się na podstawie czasowo zależnej krzywej charakterystyki operacyjnej odbiornika (ROC) oszacowanej na losowej próbce. Typową cechą złożoności danych typu „czas-do-zdarzenia” jest to, że obserwacje w próbie mogą być prawostronnie ocenzurowane. W związku z tym oszacowanie opiera się na sekwencyjnym losowym próbkowaniu i estymatorze Kaplana-Meiera.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesJournal of Finance and Financial Law;50en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectbinary classificationen
dc.subjectsequential random samplingen
dc.subjectsensitivity and specificityen
dc.subjecttime-dependent ROC curvesen
dc.subjectocena zdolności kredytowejpl
dc.subjectklasyfikacja binarnapl
dc.subjectkrzywe ROCpl
dc.subjectestymator Kaplana-Meierapl
dc.titleOptimal thresholding for binary classification applied in credit scoringen
dc.typeArticle
dc.page.number49-66
dc.contributor.authorAffiliationInstitute of Statistics and Demography, University of Lodzen
dc.identifier.eissn2353-5601
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dc.contributor.authorEmailagnieszka.rossa@uni.lodz.pl
dc.identifier.doi10.18778/2391-6478.2.50.03
dc.relation.volume2


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