论文标题
采样后众包数据允许可靠的统计推断：尼日利亚食品价格指数的情况
Post-sampling crowdsourced data to allow reliable statistical inference: the case of food price indices in Nigeria
论文作者
论文摘要
发展中国家的合理政策和决策通常受到缺乏及时和可靠数据的限制。众包数据可能为数据收集和分析提供了宝贵的替代方法。 g。在偏远和不安全的区域或传统方法困难或昂贵的情况下的可访问性差。但是，众包数据不能直接用于绘制合理的统计推断。确实，其使用涉及统计问题，因为数据不遵守任何正式的抽样设计，并且可能遭受各种非采样错误。为了克服这一点，我们建议使用一种特殊形式的划分后分层形式，在推论上下文中使用了众包数据。尼日利亚的一个例子说明了该方法的适用性。
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.
