Inference for Hawkes processes with challenging data

霍克斯过程是用于分析高影响事件序列的统计模型，例如火灾，地震，传染病和网络攻击。当某些事件的时间和/或标记缺失时，或者当数据不完整时，拟合这些模型并对拟合模型执行诊断检查是具有挑战性的。该项目旨在开发新的统计方法，以在不完整数据的情况下拟合这些模型，并检查拟合模型的拟合优度。预期成果包括记录这些方法的出版物和执行这些方法的软件包。主要的好处包括统计方法的进步和初级研究人员的培训。

The Hawkes processes are statistical models for the analysis of high-impact event sequences, such as bushfires, earthquakes, infectious diseases, and cyber attacks. When the times and/or marks are missing for some events or when the data is otherwise incomplete, it is challenging to fit these models and perform diagnostic checks on the fitted models. This project aims to develop novel statistical methods to fit these models in the presence of incomplete data and to check the goodness-of-fit of the fitted models. The expected outcomes include publications documenting these methods and software packages implementing them. The primary benefits include the advancement of statistical methodology and the training of junior research personnel.