Minimum chi-square method for estimating population size in capture-recapture experiments
Closed population capture-recapture estimation of population size is difficult under heterogeneous capture probabilities. We introduce the minimum chi-square method which can handle multi-occasion capture-recapture data. It complements likelihood methods with elements that can lead to confidence int...
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Published in | PloS one Vol. 18; no. 10; p. e0292622 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
San Francisco
Public Library of Science
12.10.2023
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
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Summary: | Closed population capture-recapture estimation of population size is difficult under heterogeneous capture probabilities. We introduce the minimum chi-square method which can handle multi-occasion capture-recapture data. It complements likelihood methods with elements that can lead to confidence intervals and assessment of goodness-of-fit. We conduct a comprehensive study on the minimum chi-square method for estimating the size of a closed population using multiple-occasion capture-recapture data under heterogeneous capture probability. We also develop two different bootstrap techniques that can be combined with any underlying estimator, be it the minimum chi-square estimator or a likelihood estimator, to perform useful inference for estimating population size. We present a simulation study on the minimum chi-square method and apply it to analyze white stork multiple capture-recapture data. Under certain conditions, the chi-square method outperforms the likelihood based methods. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 MT and LLEC are joint senior authors on this work Competing Interests: The authors have declared that no competing interests exist. |
ISSN: | 1932-6203 1932-6203 |
DOI: | 10.1371/journal.pone.0292622 |