| dc.contributor.author | Odhiambo, Joab | |
| dc.date.accessioned | 2023-04-24T12:55:13Z | |
| dc.date.available | 2023-04-24T12:55:13Z | |
| dc.date.issued | 2023-03 | |
| dc.identifier.citation | Odhiambo, J. O. (2023). Deep Learning Incorporated Bühlmann Credibility in the Modified Lee–Carter Mortality Model. Mathematical Problems in Engineering, 2023, 8543909.https://doi.org/10.1155/2023/8543909 | en_US |
| dc.identifier.uri | https://doi.org/10.1155/2023/8543909 | |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/862 | |
| dc.description.abstract | This research study proposes the inclusion of randomness or an error term in the modified Lee–Carter model, which improves the traditional Lee–Carter model for modeling and forecasting mortality risk for years in the actuarial science field. While the modified Lee–Carter model points out some of its common shortcomings, it has no distributional assumption that has been placed on the error/disturbance term. Incorporating a Gaussian distributional assumption on the error term is proposed, and then, the deep learning technique is used to obtain the parameter estimates. It is a departure from the traditional singular value decomposition estimation technique for estimating the parameters in the model. Finally, the B¨uhlmann credibility approach is
incorporated into the model to determine its forecasting precision compared to the classical Lee–Carter model before being applied in actuarial valuations. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Hindawi | en_US |
| dc.title | Deep Learning Incorporated Bu¨hlmann Credibility in the Modified Lee–Carter Mortality Model | en_US |
| dc.type | Article | en_US |
