Lee Shen-Ming, Hwang Wen-Han, de Dieu Tapsoba Jean
Department of Statistics, Feng Chia University, Taichung City, Taiwan.
Institute of Statistics, National Chung Hsing University, Taichung City, Taiwan.
Biometrics. 2016 Dec;72(4):1294-1304. doi: 10.1111/biom.12498. Epub 2016 Feb 22.
Individual covariates are commonly used in capture-recapture models as they can provide important information for population size estimation. However, in practice, one or more covariates may be missing at random for some individuals, which can lead to unreliable inference if records with missing data are treated as missing completely at random. We show that, in general, such a naive complete-case analysis in closed capture-recapture models with some covariates missing at random underestimates the population size. We develop methods for estimating regression parameters and population size using regression calibration, inverse probability weighting, and multiple imputation without any distributional assumptions about the covariates. We show that the inverse probability weighting and multiple imputation approaches are asymptotically equivalent. We present a simulation study to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. We also illustrate an analysis using data on the bird species yellow-bellied prinia collected in Hong Kong.
个体协变量在捕获-再捕获模型中经常被使用，因为它们可以为种群规模估计提供重要信息。然而，在实际中，某些个体可能会随机缺失一个或多个协变量，如果将缺失数据的记录视为完全随机缺失，这可能会导致不可靠的推断。我们表明，一般来说，在一些协变量随机缺失的封闭捕获-再捕获模型中，这种简单的完全病例分析会低估种群规模。我们开发了使用回归校准、逆概率加权和多重填补来估计回归参数和种群规模的方法，而无需对协变量做任何分布假设。我们表明逆概率加权和多重填补方法在渐近意义上是等价的。我们进行了一项模拟研究，以调查缺失协变量的影响并评估所提出方法的性能。我们还展示了一项使用在香港收集的黄腹鹪莺鸟类物种数据的分析。
