A general hidden state random walk model for animal movement

Aurélien Nicosia, Thierry Duchesne, Louis-Paul Rivest and Daniel Fortin

Computational Statistics & Data Analysis, 2017, vol. 105, issue C, 76-95

Abstract: A general hidden state random walk model is proposed to describe the movement of an animal that takes into account movement taxis with respect to features of the environment. A circular–linear process models the direction and distance between two consecutive localizations of the animal. A hidden process structure accounts for the animal’s change in movement behavior. The originality of the proposed approach is that several environmental targets can be included in the directional model. An EM algorithm that enables prediction of the hidden states of the process is devised to fit this model. An application to the analysis of the movement of caribou in Canada’s boreal forest is presented.

Keywords: Angular regression; Biased correlated random walk; Circular–linear process; Directional persistence; Directional statistical model; Filtering–smoothing algorithm; Markov model; Multi-state model; von Mises distribution (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:105:y:2017:i:c:p:76-95

DOI: 10.1016/j.csda.2016.07.009

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