Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural inferences from biotelemetry data, some features of these methods (e.g. analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioural states. To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed‐membership method for movement (M4), which is available within the open‐source The Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behaviour than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g. truncated normal, beta, uniform). Three behavioural states were estimated from snail kite movements, which were labelled as ‘encamped’, ‘area‐restricted search’ and ‘transit’. Changes in these behaviours over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. Our nonparametric Bayesian framework estimated behavioural states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology.
This content will become publicly available on September 9, 2024
Summary
The accurate estimation of prediction errors in time series is an important problem. It immediately affects the accuracy of prediction intervals but also the quality of a number of widely used time series model selection criteria such as AIC and others. Except for simple cases, however, it is difficult or even infeasible to obtain exact analytical expressions for one-step and multi-step predictions. This may be one of the reasons that, unlike in the independent case (see Efron, 2004), until today there has been no fully established methodology for time series prediction error estimation. Starting from an approximation to the bias-variance decomposition of the squared prediction error, this work is therefore concerned with the estimation of prediction errors in both univariate and multivariate stationary time series. In particular, several estimates are developed for a general class of predictors that includes most of the popular linear, nonlinear, parametric and nonparametric time series models used in practice, where causal invertible ARMA and nonparametric AR processes are discussed as lead examples. Simulation results indicate that the proposed estimators perform quite well in finite samples. The estimates may also be used for model selection when the purpose of modeling is prediction.
more » « less- Award ID(s):
- 1934568
- NSF-PAR ID:
- 10466964
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrika
- ISSN:
- 0006-3444
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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