抽样后众包数据以进行可靠的统计推断：尼日利亚粮食价格指数案例
Post-sampling crowdsourced data to allow reliable statistical inference: the case of food price indices in Nigeria
摘要：
发展中国家的健全政策和决策往往因缺乏及时和可靠的数据而受到限制。众包数据可以为数据收集和分析提供有价值的替代方案，例如。G.偏远和不安全地区或交通不便的地区，传统方法很难或费用很高。然而，众包数据不能直接用于得出合理的统计推断。事实上，它的使用涉及统计问题，因为数据不服从任何正式的抽样设计，也可能遭受各种非抽样误差。为了克服这一点，我们建议使用一种特殊形式的后分层与众包数据重新加权之前，他们在推理的情况下使用。尼日利亚的一个例子说明了该方法的适用性。
英文摘要：
Sound policy and decision making in developing countries is often limited by the lack of timely and reliable data. Crowdsourced data may provide a valuable alternative for data collection and analysis, e. g. in remote and insecure areas or of poor accessibility where traditional methods are difficult or costly. However, crowdsourced data are not directly usable to draw sound statistical inference. Indeed, its use involves statistical problems because data do not obey any formal sampling design and may also suffer from various non-sampling errors. To overcome this, we propose the use of a special form of post-stratification with which crowdsourced data are reweighted prior their use in an inferential context. An example in Nigeria illustrates the applicability of the method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
