Cited by 288Bayesian Methods and Mixture ModelsStatistical Methods and InferenceWildlife Ecology and Conservation

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

Pohle, Jennifer, Langrock, Roland, van Beest, Floris M., Schmidt, Niels Martin·2017·Journal of Agricultural, Biological and Environmental Statistics

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.

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Crossref · 10.1007/s13253-017-0283-8

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  • For animal behavior data, formal model selection techniques tend to select more HMM states than seem biologically reasonable.

    “For animal behavior data, formal model selection techniques tend to select more HMM states than seem biologically reasonable (i.e., additional states compensate for the inability to capture all the structure in such complex data); therefore, we first considered a simple (2-state) model and then built up complexity until a reasonable goodness-of-fit was achieved, but such that the model states were”

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Authors
Pohle, J., Langrock, R., van Beest, F. M., & Schmidt, N. M.
Affiliations
  • Bielefeld University (DE)
  • Aarhus University (DK)
Corresponding author
Pohle, Jennifer
Venue
Journal of Agricultural, Biological and Environmental Statistics
Published
2017-06-05
Volume
22
Issue
3
Pages
270-293
ISSN
  • 1085-7117 (ISSN-L)
  • 1537-2693
OpenAlex
W2622738426
Cited by
288
References
27
Citations by year
2026: 29 · 2025: 37 · 2024: 38 · 2023: 40 · 2022: 43 · 2021: 43 · 2020: 22 · 2019: 21 · 2018: 10 · 2017: 4 · 2012: 1
FWCI
9.17
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Agricultural Biological and Environmental Statistics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
  • Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
  • Scopus (Elsevier) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Keywords
hidden Markov models, model order selection, number of states, model selection, information criteria, animal movement, Ovibos moschatus

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