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Multiscale Properties of Random Walk Models of Animal Movement: Lessons from Statistical Inference
journal contribution
posted on 2012-03-09, 11:20 authored by Reiichiro Kawai, Sergei PetrovskiiThe random search problem has long attracted continuous interest owing to its broad interdisciplinary range of applications, including animal foraging, facilitated target location in biological systems and human motion. In this paper, we address the issue of statistical inference for ordinary Gaussian, Pareto, tempered Pareto and fractional Gaussian random walk models, which are among the most studied random walk models proposed as the best strategy in the random search problem. Based on rigorous analysis of the local asymptotic normality property and the Fisher information, we discuss some issues in unbiased joint estimation of the model parameters, in particular, the maximum-likelihood estimation. We present that there exist both theoretical and practical difficulties in more realistic tempered Pareto and fractional Gaussian random walk models from a statistical standpoint. We discuss our findings in the context of individual animal movement and show how our results may be used to facilitate the analysis of movement data and to improve the understanding of the underlying stochastic process.
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Citation
Proceedings of the Royal Society A (in press)Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of MathematicsVersion
- AM (Accepted Manuscript)
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Proceedings of the Royal Society A (in press)Publisher
The Royal Societyeissn
1471-2946Copyright date
2012Available date
2012-03-09Publisher DOI
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http://rspa.royalsocietypublishing.org/Language
enAdministrator link
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