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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15936 |
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| _version_ | 1866918299697152000 |
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| author | Wilkie, Tessa Eckley, Idris Fearnhead, Paul Gregory, Ian |
| author_facet | Wilkie, Tessa Eckley, Idris Fearnhead, Paul Gregory, Ian |
| contents | Understanding historical datasets, such as the England and Wales infant mortality data, for local government districts can provide valuable insights into our changing society. Such analyses can prove challenging in practice, due to frequent changes in the boundaries of local government districts for which records are collected. One solution adopted in the literature to overcome such practical challenges is to pre-process data using areal interpolation to render the units consistent over the time period of focus. However, such methods are prone to errors. In this paper we introduce a novel changepoint method to detect instances where interpolation performs poorly. We demonstrate the utility of our method on original data, and also demonstrate how correcting interpolation errors can affect the clustering of the infant mortality curves. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15936 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Detecting interpolation errors in infant mortality counts in 20th Century England and Wales Wilkie, Tessa Eckley, Idris Fearnhead, Paul Gregory, Ian Applications Understanding historical datasets, such as the England and Wales infant mortality data, for local government districts can provide valuable insights into our changing society. Such analyses can prove challenging in practice, due to frequent changes in the boundaries of local government districts for which records are collected. One solution adopted in the literature to overcome such practical challenges is to pre-process data using areal interpolation to render the units consistent over the time period of focus. However, such methods are prone to errors. In this paper we introduce a novel changepoint method to detect instances where interpolation performs poorly. We demonstrate the utility of our method on original data, and also demonstrate how correcting interpolation errors can affect the clustering of the infant mortality curves. |
| title | Detecting interpolation errors in infant mortality counts in 20th Century England and Wales |
| topic | Applications |
| url | https://arxiv.org/abs/2601.15936 |