Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model

The truncated Poisson regression model is used to arrive at point and interval estimates of the size of two offender populations, i.e. drunk drivers and persons who illegally possess firearms. The dependent capture–recapture variables are constructed from Dutch police records and are counts of individual arrests for both violations. The population size estimates are derived assuming that each count is a realization of a Poisson distribution, and that the Poisson parameters are related to covariates through the truncated Poisson regression model. These assumptions are discussed in detail, and the tenability of the second assumption is assessed by evaluating the marginal residuals and performing tests on overdispersion. For the firearms example, the second assumption seems to hold well, but for the drunk drivers example there is some overdispersion. It is concluded that the method is useful, provided it is used with care.     

A Review of the Use of Conditional Likelihood in Capture‐Recapture Experiments

We present a modern perspective of the conditional likelihood approach to the analysis of capture‐recapture experiments, which shows the conditional likelihood to be a member of generalized linear model (GLM). Hence, there is the potential to apply the full range of GLM methodologies. To put this method in context, we first review some approaches to capture‐recapture experiments with heterogeneous capture probabilities in closed populations, covering parametric and non‐parametric mixture models and the use of covariates. We then review in more detail the analysis of capture‐recapture experiments when the capture probabilities depend on a covariate.     

基于人口规模和城市化水平测算的城乡建设用地需求预测研究——以辽东凤城市为例

以辽宁东部凤城市为研究区域,采用1997年至2005年凤城市的人口、经济等统计数据,从作为土地需求预测基础的社会化发展水平预测入手,利用不同的预测模型对凤城市的人口规模、城镇化水平进行预测,并通过对比各预测模型的测算数据,提出科学方案并做最优选择,最终得出适宜凤城市发展的测算数据,以此为依据预测城乡建设用地需求量,为凤城市城乡建设用地合理发展及新一轮的土地利用总体规划修编提供数据支撑和理论依据。     

Model‐Assisted Estimation of a Spatial Population Mean

This paper deals with the estimation of the mean of a spatial population. Under a design‐based approach to inference, an estimator assisted by a penalized spline regression model is proposed and studied. Proof that the estimator is design‐consistent and has a normal limiting distribution is provided. A simulation study is carried out to investigate the performance of the new estimator and its variance estimator, in terms of relative bias, efficiency, and confidence interval coverage rate. The results show that gains in efficiency over standard estimators in classical sampling theory may be impressive.