The conditionally autoregressive hidden Markov model (CarHMM): inferring behavioural states from animal tracking data exhibiting conditional autocorrelation
DOI10.1007/S13253-019-00366-2zbMath1428.62490arXiv1903.04999OpenAlexW2921931329MaRDI QIDQ2009135
Joanna Mills Flemming, Kim Whoriskey, Chris Field, Ethan Lawler, William H. Aeberhard
Publication date: 27 November 2019
Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1903.04999
hidden Markov modeldiscrete timemodel checkingautoregressive processmovement ecologymarine animal movement
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Markov processes: estimation; hidden Markov models (62M05) Animal behavior (92D50)
Uses Software
Cites Work
- Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges
- Guest editor's introduction to the special issue on ``Animal movement modeling
- Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures
- Incorporating telemetry error into hidden Markov models of animal movement using multiple imputation
- Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement
- Imputation approaches for animal movement modeling
- Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
- Some theoretical results on Markov-switching autoregressive models with gamma innovations
- Probability‐scale residuals for continuous, discrete, and censored data
- Hidden Markov Models for Time Series
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