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Activity Number:
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405
- Student Paper Award and Chambers Statistical Software Award
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #309709
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Title:
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Moving-Resting Process with Measurement Error in Animal Movement Modeling
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Author(s):
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Chaoran Hu* and Vladimir Pozdnyakov and Jun Yan and Thomas Meyer and Mark Elbroch
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Companies:
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University of Connecticut, Dept of Statistics and University of Connecticut, Dept of Statistics and University of Connecticut and University of Connecticut and Puma Program for Panthera
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Keywords:
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Composite likelihood;
Dynamic programing;
Markov process
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Abstract:
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Statistical modeling of animal movement is of critical importance. The continuous trajectory of an animal's movements is only observed at discrete, often irregularly spaced time points. Most existing models cannot handle the unequal sampling interval naturally and/or do not allow inactivity period such as resting or sleeping. The recently proposed moving-resting (MR) model is a Brownian motion governed by a telegraph process, which allows periods of inactivity in one state of the telegraph process. It is promising in modeling the movements of predators with long inactive periods such as mountain lions, but the lack of accommodation of measurement errors seriously prohibits its applications in practice. Here we incorporate measurement errors in the MR model and derive basic properties of the model. Inferences are based on a composite likelihood using the Markov property of the chain formed by every other observations. The performance of the method is validated in finite sample simulation studies. Application to the movement data of a mountain lion in Wyoming illustrates the utility of the method.
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Authors who are presenting talks have a * after their name.
