The Joint Projected and Skew Normal
@article{Mastrantonio2015TheJP, title={The Joint Projected and Skew Normal}, author={Gianluca Mastrantonio}, journal={arXiv: Applications}, year={2015} }
We introduce a new multivariate circular linear distribution suitable for modeling direction and speed in (multiple) animal movement data. To properly account for specific data features, such as heterogeneity and time dependence, a hidden Markov model is used. Parameters are estimated under a Bayesian framework and we provide computational details to implement the Markov chain Monte Carlo algorithm.
The proposed model is applied to a dataset of six free-ranging Maremma Sheepdogs. Its…
One Citation
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References
SHOWING 1-10 OF 48 REFERENCES
Bayesian hidden Markov modelling using circular‐linear general projected normal distribution
- Mathematics
- 2014
We introduce a multivariate hidden Markov model to jointly cluster time‐series observations with different support, that is, circular and linear. Relying on the general projected normal distribution,…
Directional data analysis under the general projected normal distribution.
- Computer ScienceStatistical methodology
- 2013
A tractable, parsimonious and flexible model for cylindrical data, with applications
- Computer Science
- 2015
Spatio-temporal circular models with non-separable covariance structure
- Computer Science
- 2016
This work accommodates covariates, implements full kriging and forecasting, and also allows for a nugget which can be time dependent, within a Bayesian framework, to facilitate Markov chain Monte Carlo model fitting.
Bayesian Inference for Skew-normal Linear Mixed Models
- Mathematics
- 2007
Linear mixed models (LMM) are frequently used to analyze repeated measures data, because they are more flexible to modelling the correlation within-subject, often present in this type of data. The…
Hidden Markov models for circular and linear-circular time series
- Computer ScienceEnvironmental and Ecological Statistics
- 2006
A hidden Markov model for bivariate linear-circular time series is introduced and used to describe larval movement of the fly Drosophila.
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions.
- Computer ScienceEcology
- 2012
A number of extensions of HMMs for animal movement modeling are described, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework).
A statistical model for orientation mechanism
- Mathematics
- 2001
A variance components model with response variable depending on both fixed effects of explanatory variables and random components is specified to model longitudinal circular data, in order to study…
A Unified Approach for Construction of Probability Models for Bivariate Linear and Directional Data
- Mathematics
- 2014
This article considers a unified approach based on the mixture method to construct linear bivariate models and those on the cylinder and torus involving the exponential and cardioid distributions…
Continuous-time discrete-space models for animal movement
- Computer Science
- 2015
A continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods is presented, which allows for the joint modeling of location-based as well as directional drivers of movement.