Flexible Statistical Modelling

在这个项目中，研究人员和他的同事将研究高维统计和数据科学中的选定主题。 这项工作将帮助在生物技术和其他领域工作的科学家，他们生成大规模数据集，解释和揭示他们数据中的重要模式。这将有助于科学家和医生发现许多疾病的生物学基础，并改善患者的预后和治疗选择。具体而言，这些项目将包括用于高维回归和分类的相互作用模型，用于研究小鼠和人类基因组比较的统计模型，用于平均治疗效果估计（ATE）的选择性推理方法，以及一本关于统计学中的后选择推理的研究专著。

In this project, the investigator and his colleagues will study selected topics in high-dimensional statistics and data science. This work will help scientists working in biotechnology and other areas, who generate large scale datasets, to interpret and uncover the important patterns in their data. This should help scientists and doctors to discover the biological bases of many diseases, and improve prognosis and treatment selection for patients.Specifically, these projects will include an interaction model for high-dimensional regression and classification, a statistical model for studying mouse and human genomic comparisons, a selective inference approach to average treatment effect estimation (ATE), and a research monograph on post-selection inference in statistics.