Predictive modelling in credit risk: a survival analysis case
| dc.contributor.author | Omoga, Allan Anyona | |
| dc.date.accessioned | 2017-11-20T11:36:03Z | |
| dc.date.available | 2017-11-20T11:36:03Z | |
| dc.date.issued | 2017 | |
| dc.description | Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc.SS) at Strathmore University | en_US |
| dc.description.abstract | Six survival analysis techniques are accessed by applying the techniques to a dataset consisting of 33,238 active credit facilities from a financial institution operating in Kenya. Namely, the Accelerated Failure Time (AFT) Models, Cox proportional hazard (PH) Model and the Mixture Cure Model (MCM) are considered in the comparisons. Evaluation of the techniques is conducted from a Statistical approach evaluation using the Area under the Curve (AUC) and financial evaluation using the annuity theory. The Cox Proportional Hazard (PH) and the Mixture cure model performs significantly well. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/5622 | |
| dc.language.iso | en | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.subject | Credit Event | en_US |
| dc.subject | Mixture Cure model | en_US |
| dc.subject | Survival Analysis | en_US |
| dc.subject | Credit risk modelling | en_US |
| dc.title | Predictive modelling in credit risk: a survival analysis case | en_US |
| dc.type | Thesis | en_US |
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