On the adaptive partition approach to the detection of multiple change-points.
With an adaptive partition procedure, we can partition a "time course" into consecutive non-overlapped intervals such that the population means/proportions of the observations in two adjacent intervals are significantly different at a given level . However, the widely used recursive combin...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2011-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3101215?pdf=render |
| _version_ | 1818232068245553152 |
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| author | Yinglei Lai |
| author_facet | Yinglei Lai |
| author_sort | Yinglei Lai |
| collection | DOAJ |
| description | With an adaptive partition procedure, we can partition a "time course" into consecutive non-overlapped intervals such that the population means/proportions of the observations in two adjacent intervals are significantly different at a given level . However, the widely used recursive combination or partition procedures do not guarantee a global optimization. We propose a modified dynamic programming algorithm to achieve a global optimization. Our method can provide consistent estimation results. In a comprehensive simulation study, our method shows an improved performance when it is compared to the recursive combination/partition procedures. In practice, can be determined based on a cross-validation procedure. As an application, we consider the well-known Pima Indian Diabetes data. We explore the relationship among the diabetes risk and several important variables including the plasma glucose concentration, body mass index and age. |
| first_indexed | 2024-12-12T11:00:24Z |
| format | Article |
| id | doaj.art-443dd68d73d945ad82eb64386557320f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T11:00:24Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-443dd68d73d945ad82eb64386557320f2022-12-22T00:26:33ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0165e1975410.1371/journal.pone.0019754On the adaptive partition approach to the detection of multiple change-points.Yinglei LaiWith an adaptive partition procedure, we can partition a "time course" into consecutive non-overlapped intervals such that the population means/proportions of the observations in two adjacent intervals are significantly different at a given level . However, the widely used recursive combination or partition procedures do not guarantee a global optimization. We propose a modified dynamic programming algorithm to achieve a global optimization. Our method can provide consistent estimation results. In a comprehensive simulation study, our method shows an improved performance when it is compared to the recursive combination/partition procedures. In practice, can be determined based on a cross-validation procedure. As an application, we consider the well-known Pima Indian Diabetes data. We explore the relationship among the diabetes risk and several important variables including the plasma glucose concentration, body mass index and age.http://europepmc.org/articles/PMC3101215?pdf=render |
| spellingShingle | Yinglei Lai On the adaptive partition approach to the detection of multiple change-points. PLoS ONE |
| title | On the adaptive partition approach to the detection of multiple change-points. |
| title_full | On the adaptive partition approach to the detection of multiple change-points. |
| title_fullStr | On the adaptive partition approach to the detection of multiple change-points. |
| title_full_unstemmed | On the adaptive partition approach to the detection of multiple change-points. |
| title_short | On the adaptive partition approach to the detection of multiple change-points. |
| title_sort | on the adaptive partition approach to the detection of multiple change points |
| url | http://europepmc.org/articles/PMC3101215?pdf=render |
| work_keys_str_mv | AT yingleilai ontheadaptivepartitionapproachtothedetectionofmultiplechangepoints |