Small Sample Inference for Two-way Capture Recapture Experiments

TL;DR

This paper introduces a Bayesian credible interval based on the generalized Waring distribution for small sample two-way capture recapture experiments, improving population size estimation accuracy.

Contribution

It develops a novel Bayesian approach using the generalized Waring distribution for more accurate small sample population size inference in capture-recapture studies.

Findings

Credible intervals have coverage ≥ nominal in small populations.

Interval lengths are smaller than traditional methods.

Proposed method outperforms existing confidence intervals.

Abstract

The properties of the generalized Waring distribution defined on the non negative integers are reviewed. Formulas for its moments and its mode are given. A construction as a mixture of negative binomial distributions is also presented. Then we turn to the Petersen model for estimating the population size $N$ in a two-way capture recapture experiment. We construct a Bayesian model for $N$ by combining a Waring prior with the hypergeometric distribution for the number of units caught twice in the experiment. Credible intervals for $N$ are obtained using quantiles of the posterior, a generalized Waring distribution. The standard confidence interval for the population size constructed using the asymptotic variance of Petersen estimator and .5 logit transformed interval are shown to be special cases of the generalized Waring credible interval. The true coverage of this interval is shown to be bigger than or equal to its nominal converage in small populations, regardless of the capture probabilities. In addition, its length is substantially smaller than that of the .5 logit transformed interval. Thus the proposed generalized Waring credible interval appears to be the best way to quantify the uncertainty of the Petersen estimator for populations size.