Computational Statistics

计算统计

• 批准号: CRC-2015-00200
• 负责人: McNicholas, Paul
• 金额: $ 14.57万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=642784
• 关键词: Computational; Statistics

Today, increasingly larger and more complex data are being produced. Ultra high-dimensional data are among the most problematic. Statisticians and computer scientists can deal with high-dimensional data, which can contain thousands of variables. Data being produced today, however, can have tens of thousands of variables and even more. Unfortunately, methods that work for high-dimensional data are often unsuitable for ultra high-dimensional data. Computational statistics approaches for ultra high-dimensional data will be developed, focusing on methods that find homogeneous subgroups or clusters. These approaches will be applicable in any setting where ultra high-dimensional data arise, from management science to disease diagnostics and bioinformatics.

今天，越来越多的更大和更复杂的数据正在产生。超高维数据是最有问题的数据之一。统计学家和计算机科学家可以处理包含数千个变量的高维数据。然而，今天产生的数据可能有数万个变量，甚至更多。不幸的是，适用于高维数据的方法通常不适用于超高维数据。将开发超高维数据的计算统计方法，重点是找到同质子群或聚类的方法。这些方法将适用于从管理科学到疾病诊断和生物信息学的任何超高维数据出现的环境。