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CAREER: High-Dimensional M-Estimation Under Nonstandard Conditions

职业：非标准条件下的高维 M 估计

基本信息

批准号：
1941945
负责人：
Yang Ning
金额：
$ 40万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-15 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941945&HistoricalAwards=false
关键词：
CAREER Dimensional Estimation Under Nonstandard

项目摘要

Modern technology in genomics, medical research and neuroscience generates enormous amounts of data, which calls for reliable statistical analysis tools. While the past decades have witnessed a surge of research activities on the analysis of big data in statistics and data science, the existing statistical tools are not adequate to produce reliable results due to the complexity of the data structure or the manner in which the data are collected in modern applications. The project will develop novel statistical and computational tools to address the emerging challenges in modern big data and implement them within software packages. The project will benefit a broad range of researchers including biologists, epidemiologists, medical doctors and neuroscientists. The research is complemented by an equally important education and outreach plan including designing new undergraduate and graduate courses and recruiting underrepresented minorities into the summer research program. This project will develop a novel computational and statistical framework for high-dimensional M-estimation under two types of nonstandard conditions. In the first project, the principal investigator will consider high-dimensional M-estimation with non-smooth loss functions (e.g. indicator function). The discontinuity of the loss function leads to nonstandard theory and requires new statistical methods equipped with more refined theoretical analysis. In the second project, the principal investigator will consider M-estimation subject to measurement constraints in the sense that the outcomes are only collected in a very small subset of a big dataset. The principal investigator will develop scalable computational algorithms and statistically valid estimation/inference procedures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基因组学、医学研究和神经科学领域的现代技术产生了大量数据，这就需要可靠的统计分析工具。虽然在过去几十年中，统计和数据科学中大量数据分析的研究活动激增，但由于数据结构的复杂性或现代应用中收集数据的方式，现有的统计工具不足以产生可靠的结果。该项目将开发新的统计和计算工具，以应对现代大数据中新出现的挑战，并在软件包中实施这些工具。该项目将使广泛的研究人员受益，包括生物学家，流行病学家，医生和神经科学家。这项研究得到了同样重要的教育和推广计划的补充，包括设计新的本科和研究生课程，并招募代表性不足的少数民族参加夏季研究计划。本计画将针对两种非标准条件下的高维M-估计，发展一种新的计算与统计架构。在第一个项目中，主要研究者将考虑具有非光滑损失函数（例如指示函数）的高维M估计。损失函数的不连续性导致非标准理论，需要新的统计方法配备更精细的理论分析。在第二个项目中，主要研究者将考虑受测量约束的M-估计，因为结果仅收集在大数据集的一个非常小的子集中。主要研究者将开发可扩展的计算算法和统计上有效的估计/推理程序。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。