Statistical Modelling and Inference with Complex Data

从自然科学到工程学，从健康科学到人文科学和社会科学，各个领域的研究都大大受益于统计科学。然而，构成流行统计软件包中实施的大多数技术的许多常用推理程序假定数据是理论上理想的格式，例如，独立和相同分布的观察值的集合。不幸的是，这一假设在现实生活研究中往往不现实，因此对输出的传统解释可能会产生误导。一些研究人员观察到，他们的数据的复杂结构使现有的统计工具失效，但他们没有替代方法。

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.