Space–time zero-inflated count models of Harbor seals
Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle ’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice g...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.175.1975 2023-05-15T16:33:08+02:00 Space–time zero-inflated count models of Harbor seals Jay M. Ver Hoef John K. Jansen The Pennsylvania State University CiteSeerX Archives 2007 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.175.1975 http://www.alaskafisheries.noaa.gov/protectedresources/seals/harbor/reports/spacetime_countmodels_0807.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.175.1975 http://www.alaskafisheries.noaa.gov/protectedresources/seals/harbor/reports/spacetime_countmodels_0807.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.alaskafisheries.noaa.gov/protectedresources/seals/harbor/reports/spacetime_countmodels_0807.pdf text 2007 ftciteseerx 2016-01-07T16:11:41Z Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle ’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models. Text harbor seal Yakutat Alaska Unknown |
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ftciteseerx |
language |
English |
description |
Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle ’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Jay M. Ver Hoef John K. Jansen |
spellingShingle |
Jay M. Ver Hoef John K. Jansen Space–time zero-inflated count models of Harbor seals |
author_facet |
Jay M. Ver Hoef John K. Jansen |
author_sort |
Jay M. Ver Hoef |
title |
Space–time zero-inflated count models of Harbor seals |
title_short |
Space–time zero-inflated count models of Harbor seals |
title_full |
Space–time zero-inflated count models of Harbor seals |
title_fullStr |
Space–time zero-inflated count models of Harbor seals |
title_full_unstemmed |
Space–time zero-inflated count models of Harbor seals |
title_sort |
space–time zero-inflated count models of harbor seals |
publishDate |
2007 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.175.1975 http://www.alaskafisheries.noaa.gov/protectedresources/seals/harbor/reports/spacetime_countmodels_0807.pdf |
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harbor seal Yakutat Alaska |
genre_facet |
harbor seal Yakutat Alaska |
op_source |
http://www.alaskafisheries.noaa.gov/protectedresources/seals/harbor/reports/spacetime_countmodels_0807.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.175.1975 http://www.alaskafisheries.noaa.gov/protectedresources/seals/harbor/reports/spacetime_countmodels_0807.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766022853947293696 |