Minimum chi-square method for estimating population size in capture-recapture experiments

Yuyan Zheng, Yongfei Mao, Min Tsao and Laura L E Cowen

PLOS ONE, 2023, vol. 18, issue 10, 1-17

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0292622

DOI: 10.1371/journal.pone.0292622

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