Statistical Analysis Using Density Surrogates

使用密度替代物进行统计分析

• 批准号: 1810960
• 负责人: Yen-Chi Chen
• 金额: $ 10.08万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1810960&HistoricalAwards=false
• 关键词: Statistical; Analysis; Using; Density; Surrogates

Density surrogates are quantities that can help measure the likelihood of seeing observations with certain characteristics. This project aims at developing novel statistical approaches based on density surrogates and analyzing the underlying theoretical properties. Methodologies developed from this project will be applied to solving scientific questions such as investigating human activities using GPS data and detecting matter distribution inside our Universe.This project focuses on developing new statistical methodologies for analyzing complex data sets using density surrogates. Density surrogates are quantities similar to the probability density that characterizes the underlying distribution of the observed random sample. Examples of density surrogates include the linkage criterion in a hierarchical clustering, radius of the k-nearest neighborhood, and expected value of a kernel density estimator. This project proposes several novel density surrogates and designs new statistical tools. These new density surrogates can be used to perform statistical analysis in situations where the conventional density-based approaches fail.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.

密度替代物是可以帮助测量观测到具有某些特征的可能性的量。本项目旨在发展基于密度替代物的新型统计方法，并分析潜在的理论性质。该项目开发的方法将应用于解决科学问题，如利用GPS数据调查人类活动和探测宇宙中的物质分布。这个项目的重点是开发新的统计方法来分析使用密度替代物的复杂数据集。密度替代物是与概率密度相似的量，它表征了观察到的随机样本的潜在分布。密度代理的例子包括层次聚类中的链接准则、k近邻的半径和核密度估计器的期望值。本项目提出了几种新的密度替代物，并设计了新的统计工具。在传统的基于密度的方法失败的情况下，这些新的密度替代物可用于执行统计分析。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。

项目成果

MEASURING HUMAN ACTIVITY SPACES FROM GPS DATA WITH DENSITY RANKING AND SUMMARY CURVES

• DOI: 10.1214/19-aoas1311
• 发表时间: 2020-03-01
• 期刊: ANNALS OF APPLIED STATISTICS
• 影响因子: 1.8
• 作者: Chen, Yen-Chi;Dobra, Adrian
• 通讯作者: Dobra, Adrian

Pattern graphs: A graphical approach to nonmonotone missing data

• DOI: 10.1214/21-aos2094
• 发表时间: 2020-04
• 期刊: The Annals of Statistics
• 作者: Yen-Chi Chen

A statistical framework for measuring the temporal stability of human mobility patterns

• DOI: 10.1080/02664763.2019.1711363
• 发表时间: 2020-01-15
• 期刊: JOURNAL OF APPLIED STATISTICS
• 影响因子: 1.5
• 作者: Dong, Zhihang;Chen, Yen-Chi;Dobra, Adrian
• 通讯作者: Dobra, Adrian

Nonparametric inference via bootstrapping the debiased estimator

• DOI: 10.1214/19-ejs1575
• 发表时间: 2019-01-01
• 期刊: ELECTRONIC JOURNAL OF STATISTICS
• 影响因子: 1.1
• 作者: Cheng, Gang;Chen, Yen-Chi
• 通讯作者: Chen, Yen-Chi

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

核平滑、均值平移及其使用定向数据的学习理论

• 发表时间: 2021
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Zhang, Yikun;Chen, Yen-Chi
• 通讯作者: Chen, Yen-Chi