Statistical Modelling and Inference with Complex Data

复杂数据的统计建模和推理

• 批准号: RGPIN-2016-04346
• 负责人: Hu, Xiaoqiong
• 金额: $ 2.91万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635870
• 关键词: Statistical; Modelling; Inference; Complex; Data

Studies in fields ranging from natural sciences to engineering to health sciences to human and social sciences have benefited greatly from the statistical sciences. However, many commonly used inferential procedures, which form the majority of the techniques implemented in popular statistical software packages, assume that the data are in a theoretically ideal format, e.g., a collection of independent and identically distributed observations. Unfortunately, this assumption is often not realistic in real-life studies, and thus conventional interpretations of the output can be misleading. Some researchers have observed that the complex structures of their data invalidate existing statistical tools, but they do not have alternative approaches.

从自然科学到工程学，从健康科学到人文科学和社会科学，各个领域的研究都从统计科学中受益匪浅。然而，许多常用的推理程序，形成了流行的统计软件包中实现的大多数技术，假设数据是理论上理想的格式，例如，独立同分布观测值的集合。不幸的是，这种假设在现实生活中的研究往往是不现实的，因此对输出的传统解释可能会产生误导。一些研究人员已经观察到，他们的数据的复杂结构使现有的统计工具无效，但他们没有替代方法。