Dimension Reduction, Model Selection and Classification in Functional Data Analysis.

函数数据分析中的降维、模型选择和分类。

• 批准号: 1105634
• 负责人: John Stufken
• 金额: $ 12万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1105634&HistoricalAwards=false
• 关键词: Dimension; Reduction; Model; Selection; Classification

Functional data analysis aims to model and analyze data sets where a datum is a random function, e.g. a curve or a high dimensional image. Due to the fast growth of modern data collection methods, such data sets become more and more prevalent in many biological, medical and industrial applications. Functional data are viewed as infinite dimensional vectors in a functional space, and are usually observed on discrete points and measured with error. Due to the infinite dimensional nature of functional data, dimension reduction is essential for visualizing, modeling and making inference on these data. In the proposed project, the investigator will study new, computationally efficient dimension reduction methods for functional data based on spline approximations, and use asymptotic theory to develop new statistical devices for model selection and inference. The investigator will also study classification problems in functional data, by combining the proposed dimension reduction techniques with modern machine learning methods.The proposed research is motivated by data from colon carcinogenesis experiments, hypertension studies, AIDS clinical trials and functional magnetic resonance imaging experiments. The proposed project will benefit the society by advancing knowledge in these scientific fields. To achieve broader dissemination of the research results, the investigator will provide free and user friendly software to all scientific researchers. A new course on functional data analysis will be developed in the investigator's institute. The new course aims to nurture the ability of students to analyze real and innovative data sets and help them gain deeper understanding of modern statistical methods and theory.

函数数据分析旨在对数据集进行建模和分析，其中数据是随机函数，例如曲线或高维图像。由于现代数据收集方法的快速发展，这样的数据集在许多生物、医学和工业应用中变得越来越普遍。函数数据被视为函数空间中的无穷维向量，通常在离散点上观察并测量误差。由于函数数据的无限维性质，降维对于这些数据的可视化，建模和推理至关重要。在拟议的项目中，研究人员将研究新的，计算效率高的降维方法的基础上样条近似的功能数据，并使用渐近理论开发新的统计设备的模型选择和推理。研究人员还将研究功能数据的分类问题，通过结合所提出的降维技术与现代机器学习方法，所提出的研究是由来自结肠癌实验，高血压研究，艾滋病临床试验和功能磁共振成像实验的数据激发的。拟议的项目将通过促进这些科学领域的知识而造福社会。为了更广泛地传播研究成果，研究人员将向所有科学研究人员提供免费和用户友好的软件。将在调查员研究所开设一门关于功能数据分析的新课程。新课程旨在培养学生分析真实的和创新的数据集的能力，帮助他们加深对现代统计方法和理论的理解。