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From start to finish: a framework for the production of small area official statistics

机译:从头到尾:小范围官方统计数据的生成框架

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Small area estimation is a research area in official and survey statistics of great practical relevance for national statistical institutes and related organizations. Despite rapid developments in methodology and software, researchers and users would benefit from having practical guidelines for the process of small area estimation. We propose a general framework for the production of small area statistics that is governed by the principle of parsimony and is based on three broadly defined stages, namely specification, analysis and adaptation, and evaluation. Emphasis is given to the interaction between a user of small area statistics and the statistician in specifying the target geography and parameters in the light of the available data. Model-free and model-dependent methods are described with a focus on model selection and testing, model diagnostics and adaptations such as use of data transformations. Uncertainty measures and the use of model and design-based simulations for method evaluation are also at the centre of the paper. We illustrate the application of the proposed framework by using real data for the estimation of non-linear deprivation indicators. Linear statistics, e.g. averages, are included as special cases of the general framework.
机译:小面积估计是官方统计和调查统计中的一个研究领域,对国家统计机构和相关组织具有重大现实意义。尽管方法和软件得到了快速发展,但研究人员和用户将受益于针对小面积估算过程的实用指南。我们提出了一个基于简约原则的小区域统计数据生成的通用框架,该框架基于三个广泛定义的阶段,即规范,分析和适应以及评估。在根据可用数据指定目标地理位置和参数时,重点要放在小区域统计的用户与统计员之间的交互上。描述了无模型且依赖模型的方法,重点是模型选择和测试,模型诊断和改编(例如使用数据转换)。不确定性措施以及使用基于模型和基于设计的仿真进行方法评估也是本文的重点。我们通过使用真实数据估计非线性剥夺指标来说明所提出框架的应用。线性统计,例如平均数作为一般框架的特殊情况包括在内。

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