In this paper we attempt to answer the following question: “Is it possible to obtain reliable estimates for the prevalence of anemia rates in children under five years in the districts of Peru?” Specifically, the objective of the present paper is to understand to which extent employing the basic and the spatial Fay–Herriot models can compensate for inadequate sample size in most of the sampled districts, and whether the way of choosing the spatial neighbors has an impact on the resulting inference. Furthermore, we explore the question of how to choose an optimal way to define the neighbors. As such, our research focuses on studying the prediction accuracy of the aforementioned models, and on the sensitivity of the results to the definition of “neighbor”. We use the data from the Demographic and Family Health Survey of the year 2019, and the National Census carried out in 2017.

在本文中，我们试图回答以下问题：“是否有可能获得秘鲁各地区五岁以下儿童贫血患病率的可靠估计数？” 具体而言，本文的目的是了解在多大程度上采用基本模型和空间 Fay-Herriot 模型可以弥补大多数抽样地区的样本量不足，以及选择空间邻居的方式是否会影响由此产生的推论。此外，我们探讨了如何选择最佳方式来定义邻居的问题。因此，我们的研究重点是研究上述模型的预测准确性，以及结果对“邻居”定义的敏感性。我们使用 2019 年人口和家庭健康调查的数据，