Multiple systems estimation refers to a class of inference procedures that are commonly used to estimate the size of hidden populations based on administrative lists. In this paper we discuss some of the common challenges encountered in such studies. In particular, we summarize theoretical issues relating to the existence of maximum likelihood estimators, model identifiability, and parameter redundancy when there is sparse overlap among the lists. We also discuss techniques for matching records when there are no unique identifiers, exploiting covariate information to improve estimation, and addressing missing data. We offer suggestions for remedial actions when these issues/challenges manifest. The corresponding R coding packages that can assist with the analyses of multiple systems estimation data sets are also discussed.

多系统估计是指一类推理过程，通常用于基于管理列表来估计隐藏人口的数量。在本文中，我们讨论了此类研究中遇到的一些常见挑战。特别是，当列表之间存在稀疏重叠时，我们总结了与最大似然估计器的存在，模型可识别性和参数冗余有关的理论问题。我们还将讨论在没有唯一标识符时匹配记录，利用协变量信息来改进估计以及解决丢失数据的技术。当这些问题/挑战显现时，我们提供了补救措施的建议。还讨论了可以帮助分析多个系统估计数据集的相应R编码包。