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利用创新的贝叶斯信息先验规范方法提高信用评分模型的准确性

IF 2.7 4区 管理学 Q2 MANAGEMENT
Journal of the Operational Research Society Pub Date : 2024-04-19 DOI:10.1080/01605682.2024.2339510
Zheqi Wang, Jonathan Crook, Galina Andreeva
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引用次数: 0

摘要

本文提出了一种新的贝叶斯信息先验规范方法（BAF方法--使用ARIMA预测的贝叶斯先验），以在信用风险建模中引入额外信息，并改进建模方法。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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Improving the accuracy of credit scoring models using an innovative Bayesian informative prior specification method
A new Bayesian informative prior specification method (BAF method – Bayesian priors using ARIMA forecasts) is proposed to introduce additional information into credit risk modelling and improve mod...
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来源期刊
Journal of the Operational Research Society
Journal of the Operational Research Society 管理科学-运筹学与管理科学
CiteScore
6.80
自引率
13.90%
发文量
144
审稿时长
7.3 months
期刊介绍： JORS is an official journal of the Operational Research Society and publishes original research papers which cover the theory, practice, history or methodology of OR.
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