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A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data. / McCrea, Rachel; Jeyam, Anita; Pradel, Roger.
In: Frontiers in Ecology and Evolution, Vol. 9, 598325, 23.02.2021.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

McCrea, R, Jeyam, A & Pradel, R 2021, 'A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data', Frontiers in Ecology and Evolution, vol. 9, 598325. https://doi.org/10.3389/fevo.2021.598325

APA

McCrea, R., Jeyam, A., & Pradel, R. (2021). A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data. Frontiers in Ecology and Evolution, 9, Article 598325. https://doi.org/10.3389/fevo.2021.598325

Vancouver

McCrea R, Jeyam A, Pradel R. A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data. Frontiers in Ecology and Evolution. 2021 Feb 23;9:598325. doi: 10.3389/fevo.2021.598325

Author

McCrea, Rachel ; Jeyam, Anita ; Pradel, Roger. / A Test for the Underlying State-Structure of Hidden Markov Models : Partially Observed Capture-Recapture Data. In: Frontiers in Ecology and Evolution. 2021 ; Vol. 9.

Bibtex

@article{180a3dd53c4048c2b2a198012c5cf2cb,
title = "A Test for the Underlying State-Structure of Hidden Markov Models: Partially Observed Capture-Recapture Data",
abstract = "Hidden Markov models (HMMs) are being widely used in the field of ecological modeling, however determining the number of underlying states in an HMM remains a challenge. Here we examine a special case of capture-recapture models for open populations, where some animals are observed but it is not possible to ascertain their state (partial observations), whilst the other animals' states are assigned without error (complete observations). We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states observed in the complete observations. We demonstrate the good performance of the test using simulation and through application to a data set of Canada Geese.",
author = "Rachel McCrea and Anita Jeyam and Roger Pradel",
year = "2021",
month = feb,
day = "23",
doi = "10.3389/fevo.2021.598325",
language = "English",
volume = "9",
journal = "Frontiers in Ecology and Evolution",
issn = "2296-701X",
publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - A Test for the Underlying State-Structure of Hidden Markov Models

T2 - Partially Observed Capture-Recapture Data

AU - McCrea, Rachel

AU - Jeyam, Anita

AU - Pradel, Roger

PY - 2021/2/23

Y1 - 2021/2/23

N2 - Hidden Markov models (HMMs) are being widely used in the field of ecological modeling, however determining the number of underlying states in an HMM remains a challenge. Here we examine a special case of capture-recapture models for open populations, where some animals are observed but it is not possible to ascertain their state (partial observations), whilst the other animals' states are assigned without error (complete observations). We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states observed in the complete observations. We demonstrate the good performance of the test using simulation and through application to a data set of Canada Geese.

AB - Hidden Markov models (HMMs) are being widely used in the field of ecological modeling, however determining the number of underlying states in an HMM remains a challenge. Here we examine a special case of capture-recapture models for open populations, where some animals are observed but it is not possible to ascertain their state (partial observations), whilst the other animals' states are assigned without error (complete observations). We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states observed in the complete observations. We demonstrate the good performance of the test using simulation and through application to a data set of Canada Geese.

UR - http://dx.doi.org/10.3389/fevo.2021.598325

U2 - 10.3389/fevo.2021.598325

DO - 10.3389/fevo.2021.598325

M3 - Journal article

VL - 9

JO - Frontiers in Ecology and Evolution

JF - Frontiers in Ecology and Evolution

SN - 2296-701X

M1 - 598325

ER -