LangrockKingMatthiopoulosEtAl2012

Reference

Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D. and Morales, J.M. (2012) Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology, 93(11):2336-2342.

Abstract

We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths.

EndNote Format

You can import this reference in EndNote.

BibTeX-CSV Format

You can import this reference in BibTeX-CSV format.

BibTeX Format

You can copy the BibTeX entry of this reference below, orimport it directly in a software like JabRef .

@ARTICLE { LangrockKingMatthiopoulosEtAl2012,
    AUTHOR = { Langrock, R. and King, R. and Matthiopoulos, J. and Thomas, L. and Fortin, D. and Morales, J.M. },
    TITLE = { Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions },
    JOURNAL = { Ecology },
    YEAR = { 2012 },
    VOLUME = { 93 },
    PAGES = { 2336-2342 },
    NUMBER = { 11 },
    ABSTRACT = { We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths. },
    KEYWORDS = { behavioral state, Bison bison, maximum likelihood, random effects, random walk, semi-Markov model, state-space model, telemetry data },
    OWNER = { amriv2 },
    TIMESTAMP = { 2012.11.21 },
}

********************************************************** ***************** Facebook Twitter *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ************* Écoles d'été et formation **************************** **********************************************************

Écoles d'été et formations

********************************************************** ***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...