Flexible estimation of the state dwell-time distribution in hidden semi-Markov models
Pohle JM, Adam T, Beumer LT. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. Computational Statistics & Data Analysis . 2022;172: 107479. Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the...
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ftubbiepub:oai:pub.uni-bielefeld.de:2963807 2023-12-17T10:31:01+01:00 Flexible estimation of the state dwell-time distribution in hidden semi-Markov models Pohle, Jennifer Marie Adam, Timo Beumer, Larissa T. 2022 https://pub.uni-bielefeld.de/record/2963807 eng eng Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2022.107479 info:eu-repo/semantics/altIdentifier/issn/0167-9473 info:eu-repo/semantics/altIdentifier/issn/1872-7352 info:eu-repo/semantics/altIdentifier/wos/000796740200005 https://pub.uni-bielefeld.de/record/2963807 info:eu-repo/semantics/closedAccess Penalized likelihood Smoothing Time series Animal movement modeling http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article doc-type:article text 2022 ftubbiepub https://doi.org/10.1016/j.csda.2022.107479 2023-11-20T00:05:37Z Pohle JM, Adam T, Beumer LT. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. Computational Statistics & Data Analysis . 2022;172: 107479. Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN. (C) 2022 Elsevier B.V. All rights reserved. Article in Journal/Newspaper Greenland muskox PUB - Publications at Bielefeld University Greenland Computational Statistics & Data Analysis 172 107479 |
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Open Polar |
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PUB - Publications at Bielefeld University |
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ftubbiepub |
language |
English |
topic |
Penalized likelihood Smoothing Time series Animal movement modeling |
spellingShingle |
Penalized likelihood Smoothing Time series Animal movement modeling Pohle, Jennifer Marie Adam, Timo Beumer, Larissa T. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models |
topic_facet |
Penalized likelihood Smoothing Time series Animal movement modeling |
description |
Pohle JM, Adam T, Beumer LT. Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. Computational Statistics & Data Analysis . 2022;172: 107479. Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN. (C) 2022 Elsevier B.V. All rights reserved. |
format |
Article in Journal/Newspaper |
author |
Pohle, Jennifer Marie Adam, Timo Beumer, Larissa T. |
author_facet |
Pohle, Jennifer Marie Adam, Timo Beumer, Larissa T. |
author_sort |
Pohle, Jennifer Marie |
title |
Flexible estimation of the state dwell-time distribution in hidden semi-Markov models |
title_short |
Flexible estimation of the state dwell-time distribution in hidden semi-Markov models |
title_full |
Flexible estimation of the state dwell-time distribution in hidden semi-Markov models |
title_fullStr |
Flexible estimation of the state dwell-time distribution in hidden semi-Markov models |
title_full_unstemmed |
Flexible estimation of the state dwell-time distribution in hidden semi-Markov models |
title_sort |
flexible estimation of the state dwell-time distribution in hidden semi-markov models |
publisher |
Elsevier |
publishDate |
2022 |
url |
https://pub.uni-bielefeld.de/record/2963807 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland muskox |
genre_facet |
Greenland muskox |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2022.107479 info:eu-repo/semantics/altIdentifier/issn/0167-9473 info:eu-repo/semantics/altIdentifier/issn/1872-7352 info:eu-repo/semantics/altIdentifier/wos/000796740200005 https://pub.uni-bielefeld.de/record/2963807 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1016/j.csda.2022.107479 |
container_title |
Computational Statistics & Data Analysis |
container_volume |
172 |
container_start_page |
107479 |
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1785584158202920960 |