Statistical models for dependent trajectories with application to animal movement
2017 Fall. Includes bibliographical references. In this dissertation, I present novel methodology to study the way animals interact with each other and the landscape they inhabit. I propose two statistical models for dependent trajectories in which depedencies among paths arise from pairwise relatio...
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ftcolostateunidc:oai:mountainscholar.org:10217/185778 2023-06-11T04:09:39+02:00 Statistical models for dependent trajectories with application to animal movement Scharf, Henry R. Hooten, Mevin B. Cooley, Daniel S. Fosdick, Bailey K. Hobbs, N. Thompson 2018-01-17T16:46:21Z born digital doctoral dissertations application/pdf https://hdl.handle.net/10217/185778 English eng eng Colorado State University. Libraries 2000-2019 - CSU Theses and Dissertations Scharf_colostate_0053A_14611.pdf https://hdl.handle.net/10217/185778 Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Text 2018 ftcolostateunidc 2023-05-04T17:37:26Z 2017 Fall. Includes bibliographical references. In this dissertation, I present novel methodology to study the way animals interact with each other and the landscape they inhabit. I propose two statistical models for dependent trajectories in which depedencies among paths arise from pairwise relationships defined using latent dynamic networks. The first model for dependent trajectories is formulated in a discrete-time framework. The model allows researchers to make inference on a latent social network that describes pairwise connections among actors in the population, as well as parameters that govern the type of behavior induced by the social network. The second model for dependent trajectories is formulated in a continuous-time framework and is motivated primarily by reducing uncertainty in interpolations of the continuous trajectories by leveraging positive dependence among individuals. Both models are used in applications to killer whales. In addition to the two models for multiple trajectories, I introduce a new model for the movement of an individual showing a preference for areas in a landscape near a complex-shaped, dynamic feature. To facilitate estimation, I propose an approximation technique that exploits of locally linear structure in the feature of interest. I demonstrate the model for the movement of an individual responding to a dynamic feature, as well as the approximation technique, in an application to polar bears for which the changing boundary of Arctic sea ice represents the relevant dynamic feature. Text Arctic Sea ice Digital Collections of Colorado (Colorado State University) Arctic |
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Open Polar |
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Digital Collections of Colorado (Colorado State University) |
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English |
description |
2017 Fall. Includes bibliographical references. In this dissertation, I present novel methodology to study the way animals interact with each other and the landscape they inhabit. I propose two statistical models for dependent trajectories in which depedencies among paths arise from pairwise relationships defined using latent dynamic networks. The first model for dependent trajectories is formulated in a discrete-time framework. The model allows researchers to make inference on a latent social network that describes pairwise connections among actors in the population, as well as parameters that govern the type of behavior induced by the social network. The second model for dependent trajectories is formulated in a continuous-time framework and is motivated primarily by reducing uncertainty in interpolations of the continuous trajectories by leveraging positive dependence among individuals. Both models are used in applications to killer whales. In addition to the two models for multiple trajectories, I introduce a new model for the movement of an individual showing a preference for areas in a landscape near a complex-shaped, dynamic feature. To facilitate estimation, I propose an approximation technique that exploits of locally linear structure in the feature of interest. I demonstrate the model for the movement of an individual responding to a dynamic feature, as well as the approximation technique, in an application to polar bears for which the changing boundary of Arctic sea ice represents the relevant dynamic feature. |
author2 |
Hooten, Mevin B. Cooley, Daniel S. Fosdick, Bailey K. Hobbs, N. Thompson |
format |
Text |
author |
Scharf, Henry R. |
spellingShingle |
Scharf, Henry R. Statistical models for dependent trajectories with application to animal movement |
author_facet |
Scharf, Henry R. |
author_sort |
Scharf, Henry R. |
title |
Statistical models for dependent trajectories with application to animal movement |
title_short |
Statistical models for dependent trajectories with application to animal movement |
title_full |
Statistical models for dependent trajectories with application to animal movement |
title_fullStr |
Statistical models for dependent trajectories with application to animal movement |
title_full_unstemmed |
Statistical models for dependent trajectories with application to animal movement |
title_sort |
statistical models for dependent trajectories with application to animal movement |
publisher |
Colorado State University. Libraries |
publishDate |
2018 |
url |
https://hdl.handle.net/10217/185778 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
2000-2019 - CSU Theses and Dissertations Scharf_colostate_0053A_14611.pdf https://hdl.handle.net/10217/185778 |
op_rights |
Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. |
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1768383611417919488 |