权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Statistical Learning Problems with Complex Stochastic Models

复杂随机模型的统计学习问题

基本信息

批准号：
1913149
负责人：
Yazhen Wang
金额：
$ 15万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913149&HistoricalAwards=false
关键词：
Statistical Learning Problems Complex Stochastic

项目摘要

Big data is having a profound impact on scientific research and knowledge discovery. And while big data poses many statistical and computational challenges, it also presents unprecedented opportunities for statistics and data science. The investigator will focus on emerging scientific problems through the development of novel statistical and computational means, and address the challenges that arise in solving data intensive complex problems. The research in this project on finance statistics and computational algorithms is motivated by solving practical problems, and will yield cutting-edge statistical techniques and effective computational tools. The investigator actively participates in activities to integrate research with student training and applies the research outcomes to fields like finance and deep learning. The investigator will conduct novel research on stochastic gradient descent algorithms and unified models for financial data. The research goals are to develop innovative statistical methodologies, computing techniques, and theories for: 1) unified stochastic models for combined inference based on both high-frequency and low-frequency financial data, and 2) statistical and computational analysis of stochastic gradient descent algorithms with applications to machine learning in particular deep learning. The investigator intends to establish theoretically-supported statistical methodologies and computational procedures, and significantly advance computational and statistical understanding to the proposed research problems.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.

大数据正在对科学研究和知识发现产生深远影响。虽然大数据带来了许多统计和计算方面的挑战，但它也为统计和数据科学带来了前所未有的机遇。研究者将专注于新兴的科学问题，通过发展新的统计和计算手段，并解决在解决数据密集型复杂问题中出现的挑战。本项目对金融统计和计算算法的研究是为了解决实际问题，并将产生前沿的统计技术和有效的计算工具。研究者积极参与活动，将研究与学生培训相结合，并将研究成果应用于金融、深度学习等领域。研究者将对随机梯度下降算法和金融数据统一模型进行新颖的研究。研究目标是开发创新的统计方法，计算技术和理论：1)基于高频和低频金融数据的组合推理的统一随机模型，以及2)随机梯度下降算法的统计和计算分析，并应用于机器学习，特别是深度学习。研究者打算建立理论支持的统计方法和计算程序，并显著推进计算和统计理解提出的研究问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。