2026-10-09T17:28:14Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34029477 2026-09-30T04:29:33Z category_792 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Bell-generated family of mixture cure rate models with application to credit default.docx Ebenezer Asabre (25141299) Suleman Nasiru (19666957) Nana Kena Frempong (11624933) Kamal Barley (234009) Simon Kojo Appiah (25141302) Applied Mathematics not elsewhere classified Bell-generated class maximum likelihood estimation mixture cure rate models simulation survival analysis time-to-default <p>Time-to-default data in credit risk modeling are often characterized by heavy tails, skewness, non-monotonic hazard functions, and a non-negligible proportion of long-term survivors. To address these features, we propose a new class of mixture cure rate models based on the Bell-generated (Bell-G) class of distributions. By integrating the Bell-G mechanism with classical baseline distributions, including the Weibull, Burr XII, log-logistic, and lognormal, we develop flexible survival models capable of capturing complex hazard dynamics and tail behavior. The proposed models naturally accommodate a cured fraction while offering enhanced flexibility in representing diverse distributional shapes. Parameter estimation is carried out using the maximum-likelihood method, and the finite-sample performance of the estimators is assessed via Monte Carlo simulations in terms of bias, root-mean-squared error and estimated cure proportions. Covariate effects on the cure fraction are incorporated via a logistic link function. An extensive empirical analysis using real credit risk datasets demonstrates that the proposed Bell-G cure rate models provide improved goodness-of-fit, as measured by standard information criteria and likelihood-based metrics, relative to conventional cure and non-cure survival models. Overall, the results highlight the practical utility of the proposed framework in capturing latent heterogeneity and enhancing risk quantification in credit risk applications.</p> 2026-09-30T04:29:33Z Dataset Dataset 10.3389/fams.2026.1873298.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Bell-generated_family_of_mixture_cure_rate_models_with_application_to_credit_default_docx/34029477 CC BY 4.0