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Multiscale Properties of Random Walk Models of Animal Movement: Lessons from Statistical Inference

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journal contribution
posted on 09.03.2012, 11:20 by Reiichiro Kawai, Sergei Petrovskii
The 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.

History

Citation

Proceedings of the Royal Society A (in press)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Mathematics

Version

AM (Accepted Manuscript)

Published in

Proceedings of the Royal Society A (in press)

Publisher

The Royal Society

eissn

1471-2946

Copyright date

2012

Available date

09/03/2012

Publisher version

http://rspa.royalsocietypublishing.org/

Language

en

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