Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement

Jennifer Pohle (), Roland Langrock, Floris M. Beest and Niels Martin Schmidt
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Jennifer Pohle: Bielefeld University
Roland Langrock: Bielefeld University
Floris M. Beest: Aarhus University
Niels Martin Schmidt: Aarhus University

Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 3, No 5, 270-293

Abstract: Abstract We discuss the notorious problem of order selection in hidden Markov models, that is of selecting an adequate number of states, highlighting typical pitfalls and practical challenges arising when analyzing real data. Extensive simulations are used to demonstrate the reasons that render order selection particularly challenging in practice despite the conceptual simplicity of the task. In particular, we demonstrate why well-established formal procedures for model selection, such as those based on standard information criteria, tend to favor models with numbers of states that are undesirably large in situations where states shall be meaningful entities. We also offer a pragmatic step-by-step approach together with comprehensive advice for how practitioners can implement order selection. Our proposed strategy is illustrated with a real-data case study on muskox movement. Supplementary materials accompanying this paper appear online.

Keywords: Animal movement; Information criteria; Selection bias; Unsupervised learning (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (24)

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DOI: 10.1007/s13253-017-0283-8

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