Topics in multivariate nonparametric and robust methods

多元非参数和稳健方法的主题

• 批准号: 327110-2009
• 负责人: Chenouri, Shojaeddin
• 金额: $ 1.09万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=497340
• 关键词: Topics; multivariate; nonparametric; robust; methods

This project will develop some novel statistical tools for the analysis of univariate, multivariate and also very high-dimensional data arising from medical research, environmental studies, images, and industrial processes. This includes for example images of faces, handwriting, human gene, world wide web, social networks, disease epidemics, cancer screening and prevention trials, neuronal, and lifetime data, etc. Some of the tools for analyzing such data involve ranking and sorting of multivariate data by using a concept of data depth. Data depth is a measure of centrality of a given high dimensional point within a high dimensional data set.

这个项目将开发一些新的统计工具，用于分析来自医学研究、环境研究、图像和工业过程的单变量、多变量和非常高维的数据。这包括例如人脸、笔迹、人类基因、万维网、社交网络、疾病流行、癌症筛查和预防试验、神经元和终生数据等的图像。用于分析这些数据的一些工具包括通过使用数据深度的概念对多变量数据进行排序和排序。数据深度是对高维数据集中给定的高维点的中心性的度量。