Rivest Louis-Paul, Baillargeon Sophie
Département de mathématiques et de statistique, Université Laval, Ste-Foy, Québec, Canada G1K 7P4.
Biometrics. 2007 Dec;63(4):999-1006. doi: 10.1111/j.1541-0420.2007.00779.x. Epub 2007 Apr 9.
This article revisits Chao's (1989, Biometrics45, 427-438) lower bound estimator for the size of a closed population in a mark-recapture experiment where the capture probabilities vary between animals (model M(h)). First, an extension of the lower bound to models featuring a time effect and heterogeneity in capture probabilities (M(th)) is proposed. The biases of these lower bounds are shown to be a function of the heterogeneity parameter for several loglinear models for M(th). Small-sample bias reduction techniques for Chao's lower bound estimator are also derived. The application of the loglinear model underlying Chao's estimator when heterogeneity has been detected in the primary periods of a robust design is then investigated. A test for the null hypothesis that Chao's loglinear model provides unbiased abundance estimators is provided. The strategy of systematically using Chao's loglinear model in the primary periods of a robust design where heterogeneity has been detected is investigated in a Monte Carlo experiment. Its impact on the estimation of the population sizes and of the survival rates is evaluated in a Monte Carlo experiment.
本文重新审视了Chao（1989年，《生物统计学》45卷，427 - 438页）在标记重捕实验中针对封闭种群规模的下限估计量，该实验中动物的捕获概率各不相同（模型M(h)）。首先，提出了将下限扩展到具有时间效应和捕获概率异质性的模型（M(th)）。对于M(th)的几个对数线性模型，这些下限的偏差被证明是异质性参数的函数。还推导了Chao下限估计量的小样本偏差减少技术。然后研究了在稳健设计的初始阶段检测到异质性时，Chao估计量所基于的对数线性模型的应用。提供了一个关于Chao对数线性模型提供无偏丰度估计量的原假设检验。在蒙特卡罗实验中研究了在已检测到异质性的稳健设计的初始阶段系统使用Chao对数线性模型的策略。在蒙特卡罗实验中评估了其对种群规模估计和存活率估计的影响。
