Statistical models for dependent trajectories with application to animal movement
| dc.contributor.author | Scharf, Henry R., author | |
| dc.contributor.author | Hooten, Mevin B., advisor | |
| dc.contributor.author | Cooley, Daniel S., committee member | |
| dc.contributor.author | Fosdick, Bailey K., committee member | |
| dc.contributor.author | Hobbs, N. Thompson, committee member | |
| dc.date.accessioned | 2018-01-17T16:46:21Z | |
| dc.date.available | 2018-01-17T16:46:21Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | 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. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Scharf_colostate_0053A_14611.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/185778 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.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. | |
| dc.title | Statistical models for dependent trajectories with application to animal movement | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Statistics | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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