Small Sample Inference for Two‐Way Capture‐Recapture Experiments

IF 1.7 3区 数学 Q1 STATISTICS & PROBABILITY
Louis‐Paul Rivest, Mamadou Yauck
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引用次数: 0

Abstract

SummaryThe properties of the generalised 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 in a two‐way capture‐recapture experiment. We construct a Bayesian model for by combining a Waring prior with the hypergeometric distribution for the number of units caught twice in the experiment. Credible intervals for are obtained using quantiles of the posterior, a generalised Waring distribution. The standard confidence interval for the population size constructed using the asymptotic variance of Petersen estimator and 0.5 logit transformed interval are shown to be special cases of the generalised 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 0.5 logit transformed interval. Thus, the proposed generalised Waring credible interval appears to be the best way to quantify the uncertainty of the Petersen estimator for populations size.
双向捕获-再捕获实验的小样本推断
摘要 回顾了定义在非负整数上的广义瓦林分布的性质。给出了其矩和模的公式。此外,还介绍了负二项分布混合分布的构造。然后,我们转向彼得森模型,以估计双向捕获-再捕获实验中的种群数量。我们结合瓦林先验和实验中两次捕获单位数的超几何分布,构建了贝叶斯模型 for。利用广义瓦林分布的后验定量值,可以得到种群数量的可信区间。使用彼得森估计器的渐近方差构建的种群数量标准置信区间和 0.5 logit 转换区间都是广义瓦林可信区间的特例。无论捕获概率如何,在小规模种群中,该区间的真实覆盖范围都大于或等于其名义平均值。此外,其长度大大小于 0.5 logit 转换区间。因此,建议的广义瓦林可信区间似乎是量化彼得森种群规模估计值不确定性的最佳方法。
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来源期刊
International Statistical Review
International Statistical Review 数学-统计学与概率论
CiteScore
4.30
自引率
5.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.
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