When samples that each cover part of a population for a certain reference date become available slowly over time, an estimate of the population size can be obtained when at least two samples are available. Ideally one uses all the available samples, but if some samples become available much later one may want to use the samples that are available earlier, to obtain a preliminary or nowcast estimate. However, a limited number of samples may no longer lead to asymptotically unbiased estimates, in particularly in case of two early available samples that suffer from pairwise dependence. In this paper we propose a multiple system nowcasting model that deals with this issue by combining the early available samples with samples from a previous reference date and the expectation-maximisation algorithm. This leads to a nowcast estimate that is asymptotically unbiased under more relaxed assumptions than the dual-system estimator. The multiple system nowcasting model is applied to the problem of estimating the number of homeless people in The Netherlands, which leads to reasonably accurate nowcast estimates.

翻译：当覆盖特定参考日期部分人口的样本随时间缓慢可用时，只要至少有两个样本可用，即可获得人口规模的估计值。理想情况下应使用所有可用样本，但若某些样本的可用时间显著滞后，则可能需要利用较早可用的样本来获得初步估计或临近预报估计。然而，有限数量的样本可能不再产生渐近无偏估计，特别是在两个较早可用样本存在成对依赖性的情况下。本文提出一种多重系统临近预报模型，通过将较早可用样本与先前参考日期的样本以及期望最大化算法相结合来解决该问题。该模型可在比双系统估计量更宽松的假设条件下，产生渐近无偏的临近预报估计。我们将多重系统临近预报模型应用于荷兰无家可归者数量的估计问题，获得了精度合理的临近预报结果。