CIF: Small: A Novel Paradigm of Information Extraction in Big Data Problems

CIF：小：大数据问题中信息提取的新范式

基本信息

批准号：
1813330
负责人：
Xiangrong Yin
金额：
$ 28.19万
依托单位：
University of Kentucky Research Foundation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2020-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813330&HistoricalAwards=false
关键词：
CIF Small Novel Paradigm Information

项目摘要

This project considers problems related to the increasing volume of big data characterized by ultra-large sample size and ultra-high dimensionality. Such volumes introduce many new and unique challenges to scientists in both computation and statistical inference. Finding and isolating the important information in big data is an extremely difficult challenge. In response to such challenges, the project will develop a novel system that can provide quality analysis of big data. With the investigator's experience in statistical research of theory, methodology, and applications, the project will provide an excellent opportunity for both undergraduate and graduate students to participate in cutting-edge statistical applications and methodology development, and thereby prepare them well for their future careers. By localizing the data, this project develops a system with a coherent collection of novel techniques for estimation, computation, asymptotic studies, and statistical inference overcoming the new challenges for big data. The project will lead to new research directions in sufficient dimension reduction (SDR) and sufficient variable selection (SVS), producing new big data mining tools applicable in a wide range of scientific fields. This work is a major step towards improving the understanding of SDR and SVS, including their advantages and how to overcome their disadvantages to suit for the challenge of big data analysis. This project will also provide scientists a new platform to develop more flexible and efficient methods. The investigator will establish theoretical properties of the proposed estimators and develop new scalable computation algorithms for ultra-size and ultra-high dimensional data with an open-access R package to disseminate the knowledge to the scientific community.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.

该项目考虑了与以超大样本量和超高维度为特征的大数据量增加有关的问题。这样的卷在计算和统计推断中为科学家带来了许多新的和独特的挑战。在大数据中查找和隔离重要信息是一个极其困难的挑战。为了应对此类挑战，该项目将开发一个可以提供大数据质量分析的新型系统。凭借研究者在理论，方法论和应用方面的统计研究经验，该项目将为本科生和研究生提供一个极好的机会，可以参与尖端的统计应用和方法论开发，从而为他们的未来职业做好准备。通过本地化数据，该项目开发了一个系统，具有连贯的新技术集合，用于估计，计算，渐近研究和统计推断克服了大数据的新挑战。该项目将导致新的研究方向缩小尺寸（SDR）和足够的变量选择（SVS），从而生产出适用于广泛科学领域的新的大数据挖掘工具。这项工作是提高对SDR和SVS的理解的主要步骤，包括他们的优势以及如何克服其缺点，以适应大数据分析的挑战。该项目还将为科学家提供一个新的平台，以开发更灵活，更有效的方法。研究人员将使用开放式R套件来建立拟议估计器的理论特性，并开发新的可扩展计算算法，以使用开放式r r包将知识传播给科学社区。该奖项反映了NSF的法定任务，并通过使用基础的智力来评估了NSF的法定任务，并值得通过评估的支持。