The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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”
From Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands (2020) · cites this paper as [72]
Citations
Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Model Selection and Multimodel Inference
(2003) · Technometrics · doi:10.1198/tech.2003.s146 · Cited by 5,509
Assessing a mixture model for clustering with the integrated completed likelihood
Christophe Biernacki, A., Celeux, G., & Govaert, G. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.865189 · Cited by 1,537
An asymptotic equivalence of choice of model by cross validation and Akaike's criterion
Stone, M. (1977) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1977.tb01603.x · Cited by 1,286
EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS
Morales, J., Haydon, D., Frair, J., Holsinger, K., & Fryxell, J. (2004) · Ecology · doi:10.1890/03-0269 · Cited by 837
Hidden Markov Models for Time Series: An Introduction Using R
Zucchini, W., & MacDonald, I. L. (2009) · Thammasat University Digital Collections · Cited by 686
Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States
Pradel, R. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00318.x · Cited by 494
An Introduction to Model Selection
Zucchini, W. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1999.1276 · Cited by 466
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions
Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., & Morales, J. M. (2012) · Ecology · doi:10.1890/11-2241.1 · Cited by 465
Self-citation
Cited by
Sorted by citations
Items 101–200 of 288
Characterizing infectious disease progression through discrete states using hidden Markov models
Ceres, K., Schukken, Y. H., & Grohn, Y. T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242683 · Cited by 9
Ricciotti, L., Picone, M., Pollice, A., & Maruotti, A. (2024) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlae065 · Cited by 8
Model‐based clustering for noisy longitudinal circular data, with application to animal movement
Ranalli, M., & Maruotti, A. (2019) · Environmetrics · doi:10.1002/env.2572 · Cited by 8
Jetter, C. N., Crossman, J. A., & Martins, E. (2023) · Endangered Species Research · doi:10.3354/esr01236 · Cited by 8
The statistical building blocks of animal movement simulations
Getz, W. M., Salter, R. M., Sethi, V., Cain, S., Spiegel, O., & Toledo, S. (2024) · Movement Ecology · doi:10.1186/s40462-024-00507-4 · Cited by 10
Cremaschini, A., Punzo, A., Martellucci, E., & Maruotti, A. (2022) · Applied Economics · doi:10.1080/00036846.2022.2117777 · Cited by 8
Berentsen, G. D., Bulla, J., Maruotti, A., & Støve, B. (2022) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12551 · Cited by 8
Harrington, K. J., Fahlbusch, J. A., Langrock, R., Therrien, J.-F., Houtz, J. L., & McDonald, B. I. (2020) · Animal Biotelemetry · doi:10.1186/s40317-020-00214-y · Cited by 7
“, 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 still biologically interpretable [72].”
BackgroundSelf-citationMapping activity of grazing cattle using commercial virtual fencing technology
Chopra, K., Cameron, T. C., Beecroft, R. C., Bristow, L., & Codling, E. A. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1536977 · Cited by 7
Shuert, C. R., Auger‐Méthé, M., Béland, K., Hussey, N. E., Desmarchelier, M. R., & Marcoux, M. (2025) · Conservation Physiology · doi:10.1093/conphys/coaf022 · Cited by 7
Generative-Discriminative Machine Learning Models for High-Frequency Financial Regime Classification
Koukorinis, A., Peters, G. W., & Germano, G. (2025) · Methodology And Computing In Applied Probability · doi:10.1007/s11009-025-10148-8 · Cited by 8
Improved order selection method for hidden Markov models: A case study with movement data
Dupont, F., Marcoux, M., Hussey, N. E., & Auger‐Méthé, M. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70025 · Cited by 7
How to build your latent Markov model: The role of time and space
Mews, S., Koslik, J.-O., & Langrock, R. (2025) · Statistical Modelling · doi:10.1177/1471082x251355681 · Cited by 7
Self-citationVonBank, J. A., Schafer, T. L. J., Cunningham, S. A., Weegman, M. D., Link, P. T., Kraai, K. J., Wikle, C. K., Collins, D. P., Cao, L., & Ballard, B. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-28937-x · Cited by 7
Validating hidden Markov models for seabird behavioural inference
Akeresola, R. A., Butler, A., Jones, E. L., King, R., Elvira, V., Black, J., & Robertson, G. (2024) · Ecology and Evolution · doi:10.1002/ece3.11116 · Cited by 6
Analysis of the Effect of Outdoor Thermal Comfort on Construction Accidents by Subcontractor Types
Song, M., Jeong, J., Kumi, L., & Mun, H. (2024) · Sustainability · doi:10.3390/su16124906 · Cited by 7
Phuksuksakul, N., Eluru, N., Haque, M. M., & Yasmin, S. (2024) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2024.100362 · Cited by 7
Schouten, R. M., Bueno, M. L. P., Duivesteijn, W., & Pechenizkiy, M. (2021) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00808-x · Cited by 7
Strange attractor of a narwhal (Monodon monoceros)
Podolskiy, E. A., & Heide‐Jørgensen, M. P. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010432 · Cited by 7
Pattison, V., Bone, C. E., Cowen, L. L. E., O’Hara, P. D., & Wilson, L. (2022) · Ibis · doi:10.1111/ibi.13173 · Cited by 7
Maximum Likelihood Estimation of Multivariate Regime Switching Student‐t Copula Models
Cortese, F. P., Pennoni, F., & Bartolucci, F. (2024) · International Statistical Review · doi:10.1111/insr.12562 · Cited by 6
Multilevel Hidden Markov Models for Behavioral Data: A Hawk-and-Dove Experiment
Maruotti, A., Fabbri, M., & Rizzolli, M. (2021) · Multivariate Behavioral Research · doi:10.1080/00273171.2021.1912583 · Cited by 6
Silvério, J. T., Tachinardi, P., Langrock, R., Kramer-Sunderbrink, A., Oda, G. A., & Valentinuzzi, V. S. (2024) · Mammalian Biology · doi:10.1007/s42991-024-00470-y · Cited by 6
Self-citationWinter movement behavior by swift foxes (Vulpes velox) at the northern edge of their range
Butler, A. R., Bly, K., Harris, H., Inman, R. M., Moehrenschlager, A., Schwalm, D. L., & Jachowski, D. S. (2019) · Canadian Journal of Zoology · doi:10.1139/cjz-2018-0272 · Cited by 6
Saravanan, P., & Menold, J. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211032860 · Cited by 7
Puliyanda, A., Li, Z., & Prasad, V. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.07.011 · Cited by 6
Dama, F., & Sinoquet, C. (2022) · Machine Learning · doi:10.1007/s10994-022-06209-5 · Cited by 6
Tardy, O., Lenglos, C., Lai, S., Berteaux, D., & Leighton, P. A. (2022) · Ecological Modelling · doi:10.1016/j.ecolmodel.2022.110207 · Cited by 6
Markov‐modulated marked Poisson processes for modeling disease dynamics based on medical claims data
Mews, S., Surmann, B., Hasemann, L., & Elkenkamp, S. (2023) · Statistics in Medicine · doi:10.1002/sim.9832 · Cited by 6
McClintock, B. T., & Lander, M. E. (2023) · Ecology · doi:10.1002/ecy.4186 · Cited by 6
Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models
Mari, R. D., Dotto, F., Farcomeni, A., & Punzo, A. (2021) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2021.1993857 · Cited by 6
Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”
Hooten, M. B., King, R., & Langrock, R. (2017) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-017-0299-0 · Cited by 6
Self-citationA hierarchical framework for segmenting movement pathsPreprint
Getz, W. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/819763 · Cited by 6
Decoding the stock market dynamics in the banking sector: Short versus long-term insights
Čeryová, B., & Árendáš, P. (2024) · The North American Journal of Economics and Finance · doi:10.1016/j.najef.2024.102311 · Cited by 5
Hidden semi-Markov models with inhomogeneous state dwell-time distributions
Koslik, J.-O. (2025) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2025.108171 · Cited by 6
A hidden Markov framework for joint identification of animal activity modes and movement phases
Cisneros‐Araujo, P., Gastón, A., Cubero, D., Pinto, D., Saura, S., & de Rivera, Ó. R. (2026) · Landscape Ecology · doi:10.1007/s10980-026-02304-3 · Cited by 5
fHMM: Hidden Markov Models for Financial Time Series in R
Oelschläger, L., Adam, T., & Michels, R. (2024) · Journal of Statistical Software · doi:10.18637/jss.v109.i09 · Cited by 5
Linking ringed seal foraging behaviour to environmental variability
Nykänen, M., Niemi, M., Biard, V., Carter, M., Pirotta, E., & Kunnasranta, M. (2025) · Movement Ecology · doi:10.1186/s40462-025-00555-4 · Cited by 5
Heit, D. R., Wilmers, C., Ortiz-Calo, W., & Montgomery, R. (2023) · Oikos · doi:10.1111/oik.09820 · Cited by 5
Segmentation of mortality surfaces by hidden Markov models
Lagona, F., & Barbi, E. (2018) · Statistical Modelling · doi:10.1177/1471082x18777806 · Cited by 5
Ranking Nursing Homes’ Performances Through a Latent Markov Model with Fixed and Random Effects
Montanari, G. E., & Doretti, M. (2018) · Social Indicators Research · doi:10.1007/s11205-018-1947-7 · Cited by 5
Precision Data-enabled Koopman-type Inverse Operators for Linear Systems
Yan, L., & Devasia, S. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.11.181 · Cited by 5
History matters: the statistical modelling of the life course
Scott, M. A., Goff, J. L., & Gauthier, J. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01648-1 · Cited by 5
Copula Modeling of Serially Correlated Multivariate Data with Hidden Structures
Zimmerman, R., Craiu, R. V., & Barajas, V. L. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2263202 · Cited by 6
Unraveling Temporal Processes using Probabilistic Graphical Models
Bueno, M. L. P. (2020) · Leiden Repository (Leiden University) · doi:10.13140/rg.2.2.15251.84000 · Cited by 5
The hidden state-dependency of transcranial alternating current stimulation (tACS)Preprint
Kasten, F. H., & Herrmann, C. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.23.423984 · Cited by 5
Movement ecology and conservation of capuchin monkeys in pine plantation landscapes
Zárate, V., Torge, I., Tujague, M. P., Baldovino, M. C., Arrabal, J. P., Vanderhoeven, E. A., Agostini, I., & Bitetti, M. S. D. (2025) · Biological Conservation · doi:10.1016/j.biocon.2025.111304 · Cited by 4
Larue, B., Farr, J. J., Ehlers, L. P. W., Herriges, J. D., Bentzen, T. W., Suitor, M. J., Joly, K., Michelot, T., Vuillaume, B., Côté, S. D., et al. (2026) · Movement Ecology · doi:10.1186/s40462-025-00621-x · Cited by 4
Multiphasic movement and step-selection patterns of dispersed tigers in the central Indian landscape
Dutta, S., & Krishnamurthy, R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309517 · Cited by 4
Hidden Markov Models for Low-Frequency Earthquake Recurrence
Allen, J., & Wang, T. (2023) · The American Statistician · doi:10.1080/00031305.2023.2282631 · Cited by 4
Bayesian Spline-Based Hidden Markov Models with Applications to Actimetry Data and Sleep Analysis
Chen, S., & Finkenstädt, B. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2279707 · Cited by 5
Hidden Markov models for multivariate panel data
Neal, M. R., Sochaniwsky, A. A., & McNicholas, P. D. (2024) · Statistics and Computing · doi:10.1007/s11222-024-10462-0 · Cited by 4
Scott, I., Aarts, E., Wannan, C. M. J., Gao, C. X., Clark, S. R., Hartmann, S., Nguyen, J., Cavve, B., Hartmann, J., Dwyer, D., et al. (2025) · Psychological Medicine · doi:10.1017/s003329172500056x · Cited by 3
Movement of Canada Geese in urban and rural areas of Iowa, USA
Luukkonen, B. Z., Klaver, R. W., & Jones, O. E. (2022) · Avian Conservation and Ecology · doi:10.5751/ace-02128-170127 · Cited by 4
An animal movement track segmentation framework for forecasting range adaptation under global change
Getz, W. M. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1171169 · Cited by 4
Subjectivity in Unsupervised Machine Learning Model Selection
Chen, W., & Cummings, M. L. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31174 · Cited by 4
Tripartite organization of brain state dynamics underlying spoken narrative comprehensionPreprint
Liu, L., Jiang, J., Li, H., & Ding, G. (2024) · eLife · doi:10.7554/elife.99997 · Cited by 4
Detecting disease progression from animal movement using hidden Markov models
Kim, D., Michelot, T., Mertes, K., Stabach, J. A., & Fieberg, J. (2026) · Journal of Applied Ecology · doi:10.1111/1365-2664.70323 · Cited by 5
The Modelling of Movement of Multiple Animals that Share Behavioural Features
Mastrantonio, G. (2022) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12561 · Cited by 3
GPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel
Scriban, A., Nabeneza, S., Cornélis, D., Delay, É., Vayssières, J., Cesaro, J., & Salgado, P. (2024) · Sensors · doi:10.3390/s24216964 · Cited by 3
Odrekhivskyі, M., Kohut, U., Kolomatskyi, V., Horbal, N., Wołowiec, T., & Dluhopolska, T. (2025) · Sustainability · doi:10.3390/su17083641 · Cited by 5
Wood, H., Tebbs, E. J., Freeman, R., Bolton, M., Cleasby, I. R., Daunt, F., Green, J. A., Newell, M. A., Newton, S., Owen, E., et al. (2025) · Animal Biotelemetry · doi:10.1186/s40317-025-00427-z · Cited by 4
do Valle, D. R., Silva, C. A., Longo, M., & Brando, P. M. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13836 · Cited by 3
SIEVE: A Space-Efficient Algorithm for Viterbi Decoding
Ciaperoni, M., Gionis, A., Katsamanis, A., & Karras, P. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3526170 · Cited by 3
Castañeda, L. B., & Arunachalam, V. (2023) · Synthesis lectures on mathematics and statistics · doi:10.1007/978-3-031-31282-3_5 · Cited by 3
Penalized composite likelihood estimation for hidden Markov models with unknown number of states
Lin, Y., & Huang, M. (2024) · Statistics & Probability Letters · doi:10.1016/j.spl.2024.110247 · Cited by 3
An information theory framework for hierarchical path segmentation and analysis of animal movement
Sethi, V., Spiegel, O., Salter, R. M., Cain, S., Toledo, S., & Getz, W. M. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103401 · Cited by 3
Kernel-based Hidden Markov Conditional Densities
Gooijer, J. G. D., Henter, G. E., & Yuan, A. (2022) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2022.107431 · Cited by 3
Behavioral responses of Silver Carp to underwater acoustic deterrent sounds
Faulkner, J. D. A., Wamboldt, J. J., Lawson, K. M., Plumb, J. M., Woodley, C. M., Stanton, J. C., Sholtis, M. D., Swyers, N. M., Roth, M. K., Urbanczyk, A. C., et al. (2025) · Transactions of the American Fisheries Society · doi:10.1093/tafafs/vnaf003 · Cited by 2
Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders
Harvey‐Carroll, J., Menéndez‐Blázquez, J., Crespo-Picazo, J. L., Sagarminaga, R., & March, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-05336-y · Cited by 2
Changes in waterfowl movement behavior in response to hunting pressure
Beatty, K. E., Huck, N. R., & Buderman, F. E. (2025) · Movement Ecology · doi:10.1186/s40462-025-00565-2 · Cited by 2
Grass carp (Ctenopharyngodon idella) movement states and backwater use can inform removal efforts
Bonjour, S. M., Hunter, R. D., Roberts, J. J., Brenden, T. O., Colborne, S. F., Faust, M. D., Mayer, C. M., Mapes, R. L., Kraus, R. T., Calfee, R. D., et al. (2025) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2025.102654 · Cited by 2
Modeling animal movement with directional persistence and attractive points
Mastrantonio, G. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1584 · Cited by 2
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models
Sidrow, E., Heckman, N., Bouchard‐Côté, A., Fortune, S. M. E., Trites, A. W., & Auger‐Méthé, M. (2024) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2024.2350476 · Cited by 2
Tapeworm infection affects sleep-like behavior in three-spined sticklebacks
Bauhus, M. B., Mews, S., Kurtz, J., Brinker, A., Peuß, R., & Anaya‐Rojas, J. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-73992-7 · Cited by 2
Are information criteria good enough to choose the right number of regimes in hidden Markov models?
Nasri, B., R'emillard, B., & Thioub, M. Y. (2023) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2024.2409381 · Cited by 3
Leacock, W. B., Smith, K. T., & Deacy, W. W. (2024) · Movement Ecology · doi:10.1186/s40462-024-00513-6 · Cited by 2
Shahzadi, A., Wang, T., Parry, M. F., & Bebbington, M. (2025) · Advances in Data Analysis and Classification · doi:10.1007/s11634-025-00632-x · Cited by 2
Movement patterns of invasive red swamp crayfish vary with sex and environmental factors
Raboin, M. J., Roth, B. M., Sullivan, A. M., Allert, A. L., Stoeckel, J. A., Nathan, L. R., Quebedeaux, K. B., Sholtis, M. D., Smerud, J. R., Erickson, R. A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-96379-8 · Cited by 2
Sidrow, E., Heckman, N., McRae, T. M., Volpov, B. L., Trites, A. W., Fortune, S. M. E., & Auger‐Méthé, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325321 · Cited by 2
Interpretable seasonal multisite hidden Markov model for stochastic rain generation in France
Emmanuel Gobet, A., Métivier, D., & Parey, S. (2025) · Advances in statistical climatology, meteorology and oceanography · doi:10.5194/ascmo-11-159-2025 · Cited by 2
Jeyam, A., McCrea, R. S., & Pradel, R. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.598325 · Cited by 2
Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models
Montanari, G. E., Doretti, M., & Marino, M. F. (2021) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00446-7 · Cited by 2
Evidence of longitudinal differences in spring migration strategies of an Arctic‐nesting goose
VonBank, J. A., Kraai, K. J., Collins, D. P., Link, P. T., Weegman, M. D., Cao, L., & Ballard, B. M. (2024) · Ecology and Evolution · doi:10.1002/ece3.11665 · Cited by 2
Koslik, J.-O., Feldmann, C., Mews, S., Michels, R., & Langrock, R. (2025) · The Annals of Applied Statistics · doi:10.1214/25-aoas2107 · Cited by 2
Self-citationIdentifying latent behavioral states in animal movement with M4, a non-parametric Bayesian methodPreprint
Cullen, J. A., Poli, C. L., Fletcher, R. J., & do Valle, D. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.05.369702 · Cited by 2
Movement ecology of white sturgeon in the regulated upper Columbia River
Jetter, C. N. (2022) · doi:10.24124/2022/59296 · Cited by 2
Moraga, S. M., Bos, F. M., Doornbos, B., Bruggeman, R., van der Krieke, L., Snippe, E., & Aarts, E. (2023) · doi:10.31234/osf.io/egp82 · Cited by 2
Jreidini, N., & Green, D. M. (2024) · Current Zoology · doi:10.1093/cz/zoae050 · Cited by 1
Baker, L. R., Gordon, M. T., Ziemba, B. P., Gershuny, V., Falke, J. J., & Bortz, D. M. (2024) · Springer eBooks · doi:10.1007/978-3-030-93954-0_50-1 · Cited by 1
Autoregressive Hidden Markov Models for High-Resolution Animal Movement Data
Stoye, F. V., Hoyer, A., & Langrock, R. (2025) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-025-00705-6 · Cited by 1
Self-citationTripartite organization of brain state dynamics underlying spoken narrative comprehension
Liu, L., Jiang, J., Li, H., & Ding, G. (2024) · eLife · doi:10.7554/elife.99997.3 · Cited by 7
Bayesian inference for the Markov-modulated Poisson process with an outcome process
Luo, Y., & Sherlock, C. (2025) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlaf021 · Cited by 1
Bez, N., Gloaguen, P., Étienne, M., Bertrand, S., Joo, R., Rivot, É., Walker, E., Woillez, M., & Mahévas, S. (2025) · Peer Community Journal · doi:10.24072/pcjournal.535 · Cited by 1
Applying movement ecology models to comparative cognition experiments: a field test in hummingbirds
Pritchard, D. J., Hurly, T. A., Photopoulou, T., & Healy, S. D. (2025) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2025.0717 · Cited by 1
Statistics for Animal Tracking Data
Barajas, V. L., Alvarez-Castro, I., & Morales, J. M. (2025) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-112723-034603 · Cited by 1
Cullen, J. A., Santos, A. J. B., White, J. W., Komoroske, L. M., Stahelin, G., & Fuentes, M. M. P. B. (2025) · Animal Biotelemetry · doi:10.1186/s40317-025-00434-0 · Cited by 1
Scale-dependent foraging behaviour and habitat associations of two sympatric marine top predators
Carter, M. I. D., Aarts, G. M., Brasseur, S., Hastie, G. D., Moss, S. E. W., Nabe‐Nielsen, J., Teilmann, J., Thompson, D., Thompson, P. M., Vincent, C., et al. (2025) · Landscape Ecology · doi:10.1007/s10980-025-02281-z · Cited by 1
Deakin, Z., Thomas, R. J., Medeiros, R., & Bolton, M. (2026) · Marine Ecology Progress Series · doi:10.3354/meps15123 · Cited by 1
Hiltunen, T. A., Kivelä, S. M., Kumpula, J., Aspi, J. O., & Welker, J. M. (2026) · Wildlife Biology · doi:10.1002/wlb3.01570 · Cited by 1
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
