Recently, multiple systems estimation (MSE) has been applied to estimate the number of victims of human trafficking in different countries. The estimation procedure consists of a log-linear analysis of a contingency table of population registers and covariates. As the number of potential models increases exponentially with the number of registers and covariates, it is practically impossible to fit and compare all models. Therefore, the model search needs to be restricted to a small subset of all potential models. This paper addresses principles and criteria for model assessment and selection for MSE of human trafficking with special attention to sparsity which is typical to human trafficking data. The concepts are illustrated on data from Slovakia and Romania.

最近，已应用多系统估计（MSE）来估计不同国家中人口贩运活动的受害者人数。估算程序包括对人口登记册和协变量列联表的对数线性分析。由于潜在模型的数量随寄存器和协变量的数量呈指数增长，因此几乎不可能拟合和比较所有模型。因此，模型搜索需要限于所有潜在模型的一小部分。本文阐述了用于人口贩运的MSE的模型评估和选择的原则和标准，尤其要注意人口贩运数据特有的稀疏性。斯洛伐克和罗马尼亚的数据说明了这些概念。