Statistical Modelling and Inference with Complex Data

从自然科学到工程学、卫生科学、人文科学和社会科学等领域的研究都极大地受益于统计科学。然而，许多常用的推断程序（在流行的统计软件包中构成了大多数技术）假设数据是理论上理想的格式，例如，独立和相同分布的观测值的集合。不幸的是，这种假设在现实生活的研究中往往是不现实的，因此对输出的传统解释可能会产生误导。一些研究人员观察到，他们的数据结构复杂，使现有的统计工具失效，但他们没有其他方法。

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.