Biostatistics for Spatial and High-Dimensional Data

随着现代生物技术的快速发展，世界正在见证数据的爆炸式增长。这些丰富的信息为解决许多最基本的科学问题提供了丰富的资源，例如，关于大脑如何工作的问题，或者遗传变异如何影响大脑的问题。尽管如此，科学界面临的一个主要挑战是现代数据集的庞大规模和复杂性，这就迫切需要严格的统计方法来理解它。神经影像学研究可能涉及描述大脑解剖、功能和连通性的大型数据集，而成像基因组学研究涉及对大脑图像和高通量基因分型产生的额外数据的综合分析。在一项全脑基因组研究中进行超过10亿次的关联测试并不罕见。在这些领域中出现的具有挑战性的统计问题的激励下，本研究将为高维环境下的时空分析开发新的方法。

With the rapid advancement of modern biotechnology, the world is witnessing an explosion of data. This wealth of information represents an abundant resource for tackling many of the most fundamental scientific questions, for example, questions about how the brain works, or how genetic variation influences the brain. Despite this, a major challenge facing the scientific community is the sheer size and complexity of modern datasets, which has created a pressing need for rigorous statistical approaches to understand it. Neuroimaging studies can involve large datasets describing the anatomy, function, and connectivity of the brain, while studies in imaging genomics involve a combined analysis of brain images and additional data arising from high-throughput genotyping. It is not unusual to conduct well over one billion tests of association in a brain-wide genome-wide study. Motivated by the challenging statistical problems arising in these areas, this research will develop new methodology for spatiotemporal analysis in high-dimensional settings.