Computational Statistics

计算统计

• 批准号: CRC-2021-00494
• 负责人: McNicholas, Paul
• 金额: $ 7.29万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758615
• 关键词: Computational; Statistics

As we move further into the data science era, the data types being produced are becoming increasingly complex. Paul McNicholas, Canada Research Chair in Computational Statistics, is developing new, computationally intensive statistical methods to gain better insight into several increasingly complex data types. In particular, work during the second term of the Chair will focus on three objectives. First of all, an approach will be developed to effectively handle outliers in three-way data, e.g., black and white images or multivariable longitudinal data. Then, work on tensor-variate distributions will be completed so that, e.g., coloured images can be effectively clustered. Finally, all of the aforementioned approaches will be implemented and made freely available as open source software. As part of the implementation process, the use of alternative algorithms will be investigated so that the most practically useful software is ultimately released.

随着我们进一步进入数据科学时代，产生的数据类型变得越来越复杂。加拿大计算统计研究主席Paul McNicholas正在开发新的计算密集型统计方法，以更好地了解几种日益复杂的数据类型。特别是，主席第二任期的工作将侧重于三个目标。首先，将开发一种方法来有效地处理三向数据中的离群值，例如，黑白白色图像或多变量纵向数据。然后，将完成张量变量分布的工作，例如，可以有效地对彩色图像进行聚类。最后，上述所有方法都将作为开放源码软件予以实施和免费提供。作为实施过程的一部分，将研究替代算法的使用，以便最终发布最实用的软件。