Hidden semi-Markov models with inhomogeneous state dwell-time distributions

TL;DR

This paper extends hidden semi-Markov models by incorporating covariate effects on dwell-time distributions, especially with periodic variations, and demonstrates their application through simulations and a muskox movement case study.

Contribution

It introduces a methodology for modeling covariate-influenced, periodically varying dwell-time distributions within HSMMs, enhancing their flexibility and applicability.

Findings

Key properties of covariate-influenced HSMMs are derived.

Simulation studies validate the model's properties and provide hyperparameter guidance.

A practical case study on muskox movement illustrates real-world applicability.

Abstract

The well-established methodology for the estimation of hidden semi-Markov models (HSMMs) as hidden Markov models (HMMs) with extended state spaces is further developed to incorporate covariate influences across all aspects of the state process model, in particular, regarding the distributions governing the state dwell time. The special case of periodically varying covariate effects on the state dwell-time distributions - and possibly the conditional transition probabilities - is examined in detail to derive important properties of such models, namely the periodically varying unconditional state distribution as well as the overall state dwell-time distribution. Through simulation studies, we ascertain key properties of these models and develop recommendations for hyperparameter settings. Furthermore, we provide a case study involving an HSMM with periodically varying dwell-time distributions to analyse the movement trajectory of an arctic muskox, demonstrating the practical relevance of the developed methodology.