Algorithms for Applied Multivariate Statistical Analysis

应用多元统计分析算法

• 批准号: 0608306
• 负责人: Alan Edelman
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0608306&HistoricalAwards=false
• 关键词: Algorithms; Applied; Multivariate; Statistical; Analysis

This project concentrates on the development, analysis, and implementation of algorithms for computing the distributions of the eigenvalues (and functions thereof) of the classical random matrix ensembles -- Wishart, Jacobi, and Laguerre. The focus is on achieving high practical efficiency by exploiting the combinatorial and algebraic properties of multivariate orthogonal polynomials as well as utilizing structured matrix computations and dynamic programming techniques.brbrThis research will provide new efficient tools for using multivariate statistical techniques in practice. Such techniques are critical in many areas and applications, including bioinformatics, genomics (population classification), wireless communications (network capacity optimization), and military applications (automatic target classification).

该项目专注于计算经典随机矩阵系综（Wishart、Jacobi 和 Laguerre）特征值（及其函数）分布的算法的开发、分析和实现。 重点是通过利用多元正交多项式的组合和代数性质以及利用结构化矩阵计算和动态编程技术来实现高实用效率。brbr这项研究将为在实践中使用多元统计技术提供新的有效工具。此类技术在许多领域和应用中都至关重要，包括生物信息学、基因组学（群体分类）、无线通信（网络容量优化）和军事应用（自动目标分类）。