Paul G Blackwell | Searcharxiv

arXiv2019

Integrated Continuous-time Hidden Markov Models

Motivated by applications in movement ecology, in this paper I propose a new class of integrated continuous-time hidden Markov models in which each observation depends on the underlying state of the process over the whole interval since the previous observation, not only on its current state. This class gives a new representation of a range of existing models, including some widely applied switching diffusion models. I show that under appropriate conditioning, a model in this class can be regarded as a conventional hidden Markov model, enabling use of the Forward Algorithm for efficient evaluation of its likelihood without sampling of its state sequence. This leads to an algorithm for inference which is more efficient, and scales better with the amount of data, than existing methods. This is demonstrated and quantified in some applications to animal movement data and some related simulation experiments.

stat.ME↗

arXiv2017

Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry amongst biostatisticians. New approaches of ever greater complexity are continue to be added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyze individual animal movement data. Specifically we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology toward what are arguably overly-complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based in mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on Lévy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but grounded in well-developed statistical practice and efficient to fit to large data sets.

stat.AP↗