Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems.

中文翻译：

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多源统计：基本情况与方法

许多国家统计机构 (NSI)，特别是在欧洲，正在从单一来源统计转向多来源统计。通过结合数据源，国家统计局可以产生更详细、更及时的统计数据，并对社会事件做出更快的反应。通过将调查数据与已有的行政数据和大数据相结合，NSI 可以节省数据收集和处理成本并减轻受访者的负担。然而，多源统计数据带来了新的问题，需要在产生足够高的输出质量和有效生成这些统计数据之前解决这些问题。使多源统计数据的生成复杂化的是，它们有许多不同的种类，因为数据集可以以许多不同的方式组合。鉴于在官方统计中生成多源统计数据的重要性迅速增加，过去几年在该领域开展了大量研究活动，并为多源统计开发了一些框架。尽管这些框架很有用，但它们通常没有给出在实践中出现的特定情况下可以应用哪种方法的指南。在本文中，我们旨在填补这一空白，构建多源统计世界及其问题，并为解决这些问题的合适方法提供一些指导。它们通常没有给出在实践中出现的特定情况下可以采用哪种方法的指导方针。在本文中，我们旨在填补这一空白，构建多源统计世界及其问题，并为解决这些问题的合适方法提供一些指导。它们通常没有给出在实践中出现的特定情况下可以采用哪种方法的指导方针。在本文中，我们旨在填补这一空白，构建多源统计世界及其问题，并为解决这些问题的合适方法提供一些指导。