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Title: Multiscale Properties of Random Walk Models of Animal Movement: Lessons from Statistical Inference
Authors: Kawai, Reiichiro
Petrovskii, Sergei
First Published: 8-Feb-2012
Publisher: The Royal Society
Citation: Proceedings of the Royal Society A (in press)
Abstract: 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.
DOI Link: 10.1098/rspa.2011.0665
eISSN: 1471-2946
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: © 2012 The Royal Society. Deposited with reference to the the journal's policy available on the SHERPA/RoMEO website.
Appears in Collections:Published Articles, Dept. of Mathematics

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