Collaborative Research: Analysis of Functional and High-Dimensional Data with Applications
协作研究:功能数据和高维数据的分析与应用
基本信息
- 批准号:0505133
- 负责人:
- 金额:$ 7.2万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2005
- 资助国家:美国
- 起止时间:2005-08-01 至 2008-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project deals with statistical inference inphenomena that result in massive, multidimensional and/orfunctional data sets. Prime examples are geophysical, biomedical,and internet related data. In addition to high dimensionality, suchdata are often characterized by self-affinity and requirenon-standard (functional) models for their modeling and subsequentstatistical analysis. The methodology to be developed will advanceboth the theory and practice of functional data analysis, a veryfast-developing and modern area of statistics. The common and novelfeatures of the statistical methods proposed here lie in the natureof analyzed data. The data sets are massive, multidimensional,functional, and possibly self-affine (fractal or multifractal).Recent progress in multiscale data representations provide naturaland efficient environments for (i) developing scale-sensitiveanalyzing tools for estimation, testing, classification, anddeconvolution, and (ii) describing, summarizing, and modelingself-similar data. Bayesian methodology will be used wheneveravailable prior information can be incorporated or whenever sensibleautomatic priors are possible.Development of new inferential methodologies is critical for thestatistical support of recent scientific initiatives and newlyemerging technologies. The proposed research is application driven,so the specificities of the application fields influence the designand focus of the methodology. Techniques suggested in the proposaldeal with problems of testing of efficiency of new medicaltreatments, target detection and classification as well asclassification of medical images, or more accurate recovery of radaror satellite data. Hence, the methodologies which result from theproposal are applicable in such areas of strategic interest ashealth and medicine and homeland security. In addition tomethodological impact, the proposed research has a strongeducational component consisting of training graduate students,involving undergraduate students in research projects, conductinginter-departmental seminars, increasing awareness of mathematicseducation among the work force, and attracting minority and femalestudents.
这个项目处理统计推断的现象,导致大量的,多维的和/或功能的数据集。主要的例子是地球物理,生物医学和互联网相关的数据。除了高维性,这些数据通常具有自相似性和非标准(函数)模型的特点,用于建模和统计分析。将要开发的方法将推进函数数据分析的理论和实践,这是一个非常快速发展的现代统计领域。这里提出的统计方法的共同和新颖的特点在于分析数据的性质。多尺度数据表示的最新进展为(i)开发用于估计、测试、分类和反卷积的尺度敏感分析工具以及(ii)描述、总结和建模自相似数据提供了自然和有效的环境。贝叶斯方法将被使用时,eneveravailable先验信息可以被纳入或whenever sensibleautomatic先验是possible.新的推理方法的发展是至关重要的统计支持最近的科学举措和新兴技术。所提出的研究是应用驱动的,因此应用领域的特殊性影响了方法的设计和重点。该提案中提出的技术涉及新医疗方法的有效性测试、目标检测和分类以及医学图像的分类,或更准确地恢复雷达或卫星数据。因此,从该提案中产生的方法适用于卫生、医学和国土安全等战略利益领域。除了方法上的影响,拟议的研究有一个强大的教育组成部分,包括培训研究生,让本科生参与研究项目,举办跨部门研讨会,提高劳动力对职业教育的认识,吸引少数民族和女性学生。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Marianna Pensky其他文献
Signed Diverse Multiplex Networks: Clustering and Inference
- DOI:
10.48550/arxiv.2402.10242 - 发表时间:
2024-02 - 期刊:
- 影响因子:0
- 作者:
Marianna Pensky - 通讯作者:
Marianna Pensky
ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network
ALMA:聚类混合多层网络的交替最小化算法
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:6
- 作者:
Xing Fan;Marianna Pensky;Feng Yu;Teng Zhang - 通讯作者:
Teng Zhang
Marianna Pensky的其他文献
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{{ truncateString('Marianna Pensky', 18)}}的其他基金
Multiplex Generalized Dot Product Graph networks: theory and applications
多重广义点积图网络:理论与应用
- 批准号:
2310881 - 财政年份:2023
- 资助金额:
$ 7.2万 - 项目类别:
Standard Grant
Statistical Inference for Multilayer Network Data with Applications
多层网络数据的统计推断及其应用
- 批准号:
2014928 - 财政年份:2020
- 资助金额:
$ 7.2万 - 项目类别:
Standard Grant
Non-Parametric Methods for Analysis of Time-Varying Network Data
时变网络数据分析的非参数方法
- 批准号:
1712977 - 财政年份:2017
- 资助金额:
$ 7.2万 - 项目类别:
Standard Grant
Solution of Sparse High-Dimensional Linear Inverse problems with Application to Analysis of Dynamic Contrast Enhanced Imaging Data
稀疏高维线性反问题的求解及其在动态对比度增强成像数据分析中的应用
- 批准号:
1407475 - 财政年份:2014
- 资助金额:
$ 7.2万 - 项目类别:
Continuing Grant
Laplace Deconvolution and Its Application to Analysis of Dynamic Contrast Enhanced Computed Tomography Data
拉普拉斯反卷积及其在动态对比增强计算机断层扫描数据分析中的应用
- 批准号:
1106564 - 财政年份:2011
- 资助金额:
$ 7.2万 - 项目类别:
Standard Grant
FRG: Collaborative Research: Overcomplete Representations with Incomplete Data: Theory, Algorithms, and Signal Processing Applications
FRG:协作研究:不完整数据的过完整表示:理论、算法和信号处理应用
- 批准号:
0652624 - 财政年份:2007
- 资助金额:
$ 7.2万 - 项目类别:
Continuing Grant
Statistical Modeling in Wavelet Domain with Application in Turbulence
小波域统计建模及其在湍流中的应用
- 批准号:
0004173 - 财政年份:2000
- 资助金额:
$ 7.2万 - 项目类别:
Standard Grant
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