Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference-Reference-Cited by-同舟云学术

Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference

Published:2022-05-14 Issue:1 Volume:38 Page:149-169
ISSN:0943-4062
Container-title:Computational Statistics
language:en
Short-container-title:Comput Stat

Author:

El Annas Monir^ORCID,Benyacoub Badreddine,Ouzineb Mohamed

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s00180-022-01220-9.pdf

Reference36 articles.

1. Anderson R (2007) The credit scoring toolkit: theory and practice for retail credit risk management and decision automation. Oxford University Press, Oxford

2. Anderson B (2019) Using Bayesian networks to perform reject inference. Expert Syst Appl 137:349–356

3. Banasik J, Crook J (2007) Reject inference, augmentation, and sample selection. Eur J Oper Res 183(3):1582–1594

4. Banasik J, Crook J (2010) Reject inference in survival analysis by augmentation. J Oper Res Soc 61(3):473–485

5. Banasik J, Crook J, Thomas LC (2003) Sample selection bias in credit scoring models. JORS 54(8):822–832

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