Collaborative Research: High Dimensional Multivariate Analysis

合作研究：高维多元分析

• 批准号: 1309156
• 负责人: Ping-Shou Zhong
• 金额: $ 5万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1309156&HistoricalAwards=false
• 关键词: Collaborative; Research; Dimensional; Multivariate; Analysis

Classical multivariate analyses are typically designed for fixed dimensional data, and are often not applicable, even invalid, for high-dimensional data. Analysis of modern high-dimensional data call for novel multivariate statistical approaches in the "large-p, small n" paradigm. This proposal consists of three broad objectives aiming to establish a set of multivariate inferential procedures that are adaptive to high dimensionality, and can accommodate a wide range of model and dependence structures. The investigators will develop new thresholding methods to improve the power performance of the tests for high dimensional means and covariance when the signals are sparse and faint. They also propose bandwidth estimators for the state of the art banding and tapering estimators for covariances, and hence make these two estimation approaches practical. The project will also develop tests for nonparametric functions of high-dimensional covariates in partially linear models. The multivariate testing procedures obtained from the proposed projects will be readily applicable in selecting gene-sets which are associated with phenotype variations, or responsive to certain treatments in the forms of having different means or covariance. The successful application in gene-set analysis will enhance our understanding of gene regulations at a biological meaningful pathway level. The research will also improve the applications of multivariate analysis in biology, marketing research, and financial risk management.

经典的多变量分析通常是为固定维度数据设计的，对于高维数据通常不适用，甚至无效。现代高维数据的分析需要在“大p，小n”范式中采用新的多元统计方法。该建议包括三个广泛的目标，旨在建立一套适应高维的多变量推理过程，并能适应广泛的模型和依赖结构。研究人员将开发新的阈值方法，以改善信号稀疏和微弱时高维均值和协方差测试的功率性能。他们还提出了最先进的带宽估计器，用于协方差的带状估计器和锥形估计器，从而使这两种估计方法实用。该项目还将开发部分线性模型中高维协变量的非参数函数的测试。从提议的项目中获得的多变量测试程序将很容易适用于选择与表型变异相关的基因集，或以具有不同手段或协方差的形式对某些处理作出反应。基因集分析的成功应用将增强我们对基因调控在生物学意义通路水平上的理解。该研究还将提高多元分析在生物学、市场研究和金融风险管理中的应用。