Mixture cure models in prediction of time to default : comparison with logit and cox models

Ewa Wycinka , Tomasz Jurkiewicz

Abstract

Mixture cure models are an extension of the standard survival models used for predicting survivors in the case of two distinct subpopulations [Sy and Taylor (Biometrics 56: 227–236, 2000)]. The models assume that the studied population is a mixture of susceptible individuals, who may experience the event of interest, and non-susceptible individuals, who will never experience it [Corbière and Joly (Comput Methods Prog Biomed 85(2): 173–180, 2007)]. Mixture cure models were used for the first time in medical statistics to model long-term survival of cancer patients. Tong et al. (Eur J Oper Res 218(1): 132–139, 2012) introduced mixture cure models to the area of credit scoring, where a large proportion of the accounts do not experience default during the loan term. In this paper, we investigate the use of a mixture cure model for a sample of 5000 consumer credit accounts from a 60-month personal loans portfolio of a Polish financial institution. All loans have been observed for 24 months. Default is the event of interest, whereas earlier repayment is considered to be censoring. We develop and compare default prediction models using the logistic regression, Cox model and mixture cure approaches. Similarities with and differences to the study results obtained by Tong et al. (Eur J Oper Res 218(1): 132–139, 2012) are scrutinised. The final discussion focuses on the usefulness of mixture cure models in predicting the probability of default, and the limitations of these models.
Author Ewa Wycinka (FM / DS)
Ewa Wycinka,,
- Department of Statistics
, Tomasz Jurkiewicz (FM / DS)
Tomasz Jurkiewicz,,
- Department of Statistics
Pages221-231
Publication size in sheets0.5
Book Jajuga Krzysztof, Orlowski Lucjan T., Staehr Karsten (eds.): Contemporary trends and challenges in finance : proceedings from the 2nd Wroclaw International Conference in Finance, Springer Proceedings in Business and Economics, 2017, Springer, ISBN 978-3-319-54884-5, [978-3-319-54885-2], 333 p., DOI:10.1007/978-3-319-54885-2
DOIDOI:10.1007/978-3-319-54885-2_21
URL https://link.springer.com/content/pdf/10.1007%2F978-3-319-54885-2_21.pdf
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 25-02-2018, BookChapterSeriesAndMatConfByIndicator
Ministerial score (2013-2016) = 15.0, 25-02-2018, BookChapterSeriesAndMatConfByIndicator
Citation count*
Cite
Share Share

Get link to the record


* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
Back