Weighted compositional functional data analysis for modeling and forecasting life-table death counts
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911234292449280 |
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| author | Shang, Han Lin Haberman, Steven |
| author_facet | Shang, Han Lin Haberman, Steven |
| contents | Age-specific life-table death counts observed over time are examples of densities. Non-negativity and summability are constraints that sometimes require modifications of standard linear statistical methods. The centered log-ratio transformation presents a mapping from a constrained to a less constrained space. With a time series of densities, forecasts are more relevant to the recent data than the data from the distant past. We introduce a weighted compositional functional data analysis for modeling and forecasting life-table death counts. Our extension assigns higher weights to more recent data and provides a modeling scheme easily adapted for constraints. We illustrate our method using age-specific Swedish life-table death counts from 1751 to 2020. Compared to their unweighted counterparts, the weighted compositional data analytic method improves short-term point and interval forecast accuracies. The improved forecast accuracy could help actuaries improve the pricing of annuities and setting of reserves. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weighted compositional functional data analysis for modeling and forecasting life-table death counts Shang, Han Lin Haberman, Steven Methodology 62R10 Age-specific life-table death counts observed over time are examples of densities. Non-negativity and summability are constraints that sometimes require modifications of standard linear statistical methods. The centered log-ratio transformation presents a mapping from a constrained to a less constrained space. With a time series of densities, forecasts are more relevant to the recent data than the data from the distant past. We introduce a weighted compositional functional data analysis for modeling and forecasting life-table death counts. Our extension assigns higher weights to more recent data and provides a modeling scheme easily adapted for constraints. We illustrate our method using age-specific Swedish life-table death counts from 1751 to 2020. Compared to their unweighted counterparts, the weighted compositional data analytic method improves short-term point and interval forecast accuracies. The improved forecast accuracy could help actuaries improve the pricing of annuities and setting of reserves. |
| title | Weighted compositional functional data analysis for modeling and forecasting life-table death counts |
| topic | Methodology 62R10 |
| url | https://arxiv.org/abs/2510.22988 |