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首页> 外文期刊>Statistical Journal of the IAOS: Journal of the International Association for Official Statistics >Production processes of official statistics and analytics processes augmented by trusted smart statistics: Friends or foes?
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Production processes of official statistics and analytics processes augmented by trusted smart statistics: Friends or foes?

机译：由可信智能统计数据的官方统计和分析过程的生产过程：朋友或敌人？

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摘要

National statistical institutes are using frameworks to organise and set up their official statistical production, e.g. GSBPM. As a sequential approach of statistical production, GSBPM has become a well-established standard using deductive reasoning as analytics’ paradigm. For example, the first GSBPM steps are entirely focused on deductive reasoning based on primary data collection and are not suited for inductive reasoning applied to (already existing) secondary data (e.g. big data resulting, for example, from smart ecosystems). Taken into account the apparent potential of big data in the official statistical production, the GSBPM process needs to adapted to incorporate both complementary approaches of analytics (i.e. inductive and deductive reasoning) and, for example, through the usage of, for example, data-informed continuous evaluation at any GSBPM step. This paper discusses the limitations of GSBPM with respect to the usage of big data (using inductive reasoning as analytics’ paradigm), and also with respect to trusted smart statistics. The authors give insights on how to augment and empower current statistical production processes by analytics, and also by (trusted) smart statistics. In addition, the paper also highlights challenges and opportunities that should be addressed to embrace this major paradigm shift.
机译：国家统计机构正在使用框架来组织和建立其官方统计生产，例如， GSBPM。作为统计生产的顺序方法，GSBPM已成为使用Destuctive推理作为分析范式的既定标准。例如，第一个GSBPM步骤完全专注于基于主数据收集的演绎推理，并且不适合应用于（已经存在的）次要数据的归纳推理（例如，从智能生态系统产生的大数据）。考虑到官方统计生产中大数据的表观潜力，GSBPM过程需要适应融合分析（即归纳和演绎推理）的互补方法，并且例如通过使用例如数据 - 在任何GSBPM步骤中明智的持续评估。本文讨论了GSBPM关于大数据使用的局限性（使用归纳推理为分析'范例），也与可信智能统计数据相吻合。作者对如何通过分析增强和赋予当前统计生产流程的洞察，以及（可信任）智能统计数据。此外，本文还强调应挑战和机遇，以接受这种主要范式转变。

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