Effect of plant-capture in a capture-recapture experiment

An efficiency study is made of a plant-capture approach for estimating population size in recapture studies. The population is augmented by the insertion of a known number of planted individuals who have already been marked and whose behaviour is identical to that of other members. A comparison is made with the case where no plants are used by considering asymptotic efficiency and a simulation study.     

捕获-再捕获模型的统计学原理

捕获-再捕获模型由国外学者首创,最初用于野生动物总体规模估计,后来经过改进逐步应用于人口普查质量评估和其他统计领域。为了正确使用该模型,采取独一无二的方法,从试验背景、组格概率和边缘概率之间的关系、组格条件概率、条件多项分布和条件似然函数等方面对其进行全面解读和研究。研究表明:使用捕获-再捕获模型必须遵守三项理论原则:即总体封闭原则、个体同质原则、独立性原则;对实际问题与理论原则之间存在的差距必须做三件事情:即发现实际问题与理论原则之间的所有分歧点、评估各个分歧点问题的严重程度、寻找解决问题的办法。     

Mixture models for capture-recapture count data

The contribution investigates the problem of estimating the size of a population, also known as the missing cases problem. Suppose a registration system is targeting to identify all cases having a certain characteristic such as a specific disease (cancer, heart disease, ...), disease related condition (HIV, heroin use, ...) or a specific behavior (driving a car without license). Every case in such a registration system has a certain notification history in that it might have been identified several times (at least once) which can be understood as a particular capture-recapture situation. Typically, cases are left out which have never been listed at any occasion, and it is this frequency one wants to estimate. In this paper modelling is concentrating on the counting distribution, e.g. the distribution of the variable that counts how often a given case has been identified by the registration system. Besides very simple models like the binomial or Poisson distribution, finite (nonparametric) mixtures of these are considered providing rather flexible modelling tools. Estimation is done using maximum likelihood by means of the EM algorithm. A case study on heroin users in Bangkok in the year 2001 is completing the contribution.     

Coverage-adjusted estimators for mark-recapture in heterogeneous populations

Consideration of coverage yields a new class of estimators of population size for the standard mark-recapture model which permits heterogeneity of capture probabilities. Real data and simulation studies are used to assess these coverage-adjusted estimators. The simulations highlight the need for estimators that perform well for a wide range of values of the mean and coefficient of variation of the capture probabilities. When judged for this type of robustness, the simulations provide good grounds for preferring the new estimators to earlier ones for this model, except when the number of sampling occasions is large. A bootstrapping approach is used to estimate the standard errors of the new estimators, and to obtain confidence intervals for the population size.     

A BETA-BINOMIAL MODEL FOR ESTIMATING THE SIZE OF A HETEROGENEOUS POPULATION

This paper compares the properties of various estimators for a beta‐binomial model for estimating the size of a heterogeneous population. It is found that maximum likelihood and conditional maximum likelihood estimators perform well for a large population with a large capture proportion. The jackknife and the sample coverage estimators are biased for low capture probabilities. The performance of the martingale estimator is satisfactory, but it requires full capture histories. The Gibbs sampler and Metropolis‐Hastings algorithm provide reasonable posterior estimates for informative priors.     

Distributional results for special cases of the jolly-seber model

The multinomial-binomial approach to the Jolly-Seber capture- recapture model is used as a basis to derive explicit probability distributions for special cases of the Jolly-Seber model:no recruitment, or no mortality. Also given are the residual distributions that allow tests of these restricted models compared to the general Jolly-Seber model. Losses on capture are allowed. The special case distribution is also derived for no recruitment and no mortality, but allowing losses on capture; this is a generalized version of Darroch's closed capture-recapture model. Here, however, it was not possible to obtain a closed form residual distribution.