论文标题
生物统计学家的渐近特性教程，并应用于19个数据
A Tutorial on Asymptotic Properties for Biostatisticians with Applications to COVID-19 Data
论文作者
论文摘要
统计估计器的渐近特性在实践和理论上都起着重要作用。但是，许多统计数据中的许多渐近结果在很大程度上取决于独立和相同分布的（IID）假设，当我们具有固定设计时，这是不现实的。在本文中，我们构建了一个通用程序的路线图，用于在固定设计下得出渐近性能，并且观察结果不必成为IID。我们在许多统计应用中进一步提供了他们的应用。最后，我们将结果应用于使用Covid-19数据集的泊松回归，以证明这些结果在实践中的力量。
Asymptotic properties of statistical estimators play a significant role both in practice and in theory. However, many asymptotic results in statistics rely heavily on the independent and identically distributed (iid) assumption, which is not realistic when we have fixed designs. In this article, we build a roadmap of general procedures for deriving asymptotic properties under fixed designs and the observations need not to be iid. We further provide their applications in many statistical applications. Finally, we apply our results to Poisson regression using a COVID-19 dataset as an illustration to demonstrate the power of these results in practice.
