Modelling multi-scale state-switching functional data with hidden Markov models ...
Data sets comprised of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods of Functional Data Analysis (FDA). We detail a hierarchical approach which treats th...
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ftdatacite:10.48550/arxiv.2101.03268 2024-09-09T19:50:10+00:00 Modelling multi-scale state-switching functional data with hidden Markov models ... Sidrow, Evan Heckman, Nancy Fortune, Sarah M. E. Trites, Andrew W. Murphy, Ian Auger-Méthé, Marie 2021 https://dx.doi.org/10.48550/arxiv.2101.03268 https://arxiv.org/abs/2101.03268 unknown arXiv https://dx.doi.org/10.1002/cjs.11673 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Methodology stat.ME Applications stat.AP FOS Computer and information sciences Article article-journal Text ScholarlyArticle 2021 ftdatacite https://doi.org/10.48550/arxiv.2101.0326810.1002/cjs.11673 2024-06-17T08:32:40Z Data sets comprised of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods of Functional Data Analysis (FDA). We detail a hierarchical approach which treats the curves as observations from a hidden Markov model (HMM). The distribution of each curve is then defined by another fine-scale model which may involve auto-regression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to sequences of curves exhibiting intricate dependence structures. As a case study, we use this framework to model the fine-scale kinematic movement of a northern resident killer whale (Orcinus orca) off the coast of British Columbia, Canada. Through simulations, we show that our model produces more interpretable state estimation and more accurate parameter estimates compared to existing methods. ... : 23 pages, 8 figures, 2 tables. Supplementary material appended to submission ... Text Killer Whale Orca Orcinus orca Killer whale DataCite British Columbia ENVELOPE(-125.003,-125.003,54.000,54.000) Canada |
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topic |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
spellingShingle |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences Sidrow, Evan Heckman, Nancy Fortune, Sarah M. E. Trites, Andrew W. Murphy, Ian Auger-Méthé, Marie Modelling multi-scale state-switching functional data with hidden Markov models ... |
topic_facet |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
description |
Data sets comprised of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods of Functional Data Analysis (FDA). We detail a hierarchical approach which treats the curves as observations from a hidden Markov model (HMM). The distribution of each curve is then defined by another fine-scale model which may involve auto-regression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to sequences of curves exhibiting intricate dependence structures. As a case study, we use this framework to model the fine-scale kinematic movement of a northern resident killer whale (Orcinus orca) off the coast of British Columbia, Canada. Through simulations, we show that our model produces more interpretable state estimation and more accurate parameter estimates compared to existing methods. ... : 23 pages, 8 figures, 2 tables. Supplementary material appended to submission ... |
format |
Text |
author |
Sidrow, Evan Heckman, Nancy Fortune, Sarah M. E. Trites, Andrew W. Murphy, Ian Auger-Méthé, Marie |
author_facet |
Sidrow, Evan Heckman, Nancy Fortune, Sarah M. E. Trites, Andrew W. Murphy, Ian Auger-Méthé, Marie |
author_sort |
Sidrow, Evan |
title |
Modelling multi-scale state-switching functional data with hidden Markov models ... |
title_short |
Modelling multi-scale state-switching functional data with hidden Markov models ... |
title_full |
Modelling multi-scale state-switching functional data with hidden Markov models ... |
title_fullStr |
Modelling multi-scale state-switching functional data with hidden Markov models ... |
title_full_unstemmed |
Modelling multi-scale state-switching functional data with hidden Markov models ... |
title_sort |
modelling multi-scale state-switching functional data with hidden markov models ... |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2101.03268 https://arxiv.org/abs/2101.03268 |
long_lat |
ENVELOPE(-125.003,-125.003,54.000,54.000) |
geographic |
British Columbia Canada |
geographic_facet |
British Columbia Canada |
genre |
Killer Whale Orca Orcinus orca Killer whale |
genre_facet |
Killer Whale Orca Orcinus orca Killer whale |
op_relation |
https://dx.doi.org/10.1002/cjs.11673 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2101.0326810.1002/cjs.11673 |
_version_ |
1809919515980464128 |