Collaborative Research: A Symphony of Smoothing and Change Point Analysis

协作研究：平滑和变点分析的交响乐

• 批准号: 1916174
• 负责人: Pang Du
• 金额: $ 16万
• 依托单位: Virginia Polytechnic Institute and State University
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916174&HistoricalAwards=false
• 关键词: Collaborative; Research; Symphony; Smoothing; Change

A sudden change in a real life process is often of particular interest since the change may signal an anomaly, the onset of a disease, or an out-of-control process. What has been ignored in the literature is the co-existence of a slowly-changing component that also needs to be properly modeled to make valid statistical inference. In traditional statistics, inferring a sudden change and modeling a slowly-changing trend are often perceived as two seemingly conflicting concepts since they emphasize respectively on continuity and discontinuity of the true underlying process. Motivated by real-life applications, this project aims to develop three sets of novel statistical methods where simultaneous modeling of the sudden change and the slowly-changing trend are properly introduced to accommodate the practical needs. The statistical analysis tools developed in this project will benefit practitioners in the fields of cybersecurity, environmental sciences, financial managements, and medical studies. The results from the project will be integrated into teaching and student training, and be disseminated through publications and the distribution of R packages.This project aims to the development of statistical methods that are natural fusions of change point analysis and nonparametric smoothing. The proposed method will combine recent developments in functional data analysis and nonparametric smoothing inference, such as functional time series and functional Bahadur representation, and change point analysis tools to solve the challenging problems. The first aim of the project focuses on change point detection in variance functions along with a smoothly-changing mean trend. A novel subsampling approach will be designed to incorporate the large sample size involved. The other two aims of the project are to develop change point detection methods and derive corresponding optimal theoretical properties for multivariate probability densities and functional time series, respectively.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.

真实的生命过程中的突然变化通常是特别感兴趣的，因为该变化可能是异常、疾病发作或失控过程的信号。在文献中被忽略的是一个缓慢变化的组件的共存，也需要适当的建模，使有效的统计推断。在传统统计学中，由于强调的是真实过程的连续性和不连续性，因此推断突变和建模缓慢变化的趋势往往被视为两个看似矛盾的概念。本研究以实际应用为出发点，提出三套新颖的统计方法，分别针对突变与缓慢变化的趋势进行同步建模，以适应实际需要。该项目开发的统计分析工具将使网络安全，环境科学，财务管理和医学研究领域的从业人员受益。该项目的成果将纳入教学和学生培训，并通过出版物和分发R软件包加以传播，该项目旨在开发自然融合变点分析和非参数平滑的统计方法。所提出的方法将联合收割机功能数据分析和非参数平滑推理的最新发展，如功能时间序列和功能Bahadur表示，和变点分析工具，以解决具有挑战性的问题。该项目的第一个目标集中在变化点检测方差函数沿着平滑变化的平均趋势。将设计一种新的二次抽样方法，以纳入所涉及的大样本量。该项目的另外两个目标是开发变点检测方法，并分别为多变量概率密度和功能时间序列导出相应的最佳理论属性。该奖项反映了NSF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

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra

• DOI: 10.1177/0003702820955245
• 发表时间: 2020-10
• 期刊: Applied Spectroscopy
• 影响因子: 3.5
• 作者: Yunnan Xu;Pang Du;R. Senger;John L. Robertson;J. Pirkle

Sparse logistic regression on functional data

功能数据的稀疏逻辑回归

• DOI: 10.4310/21-sii688
• 发表时间: 2022
• 期刊: Statistics and Its Interface
• 影响因子: 0.8
• 作者: Du, Pang;Xu, Yunnan;Robertson, John;Senger, Ryan
• 通讯作者: Senger, Ryan

Surface temperature monitoring in liver procurement via functional variance change-point analysis

• DOI: 10.1214/19-aoas1297
• 发表时间: 2020-03
• 期刊: The Annals of Applied Statistics
• 作者: Zhenguo Gao;Pang Du;R. Jin;John L. Robertson

A New Method for Inferring Ground-Truth Labels and Malware Detector Effectiveness Metrics

推断真实标签和恶意软件检测器有效性指标的新方法

• DOI: 10.1007/978-3-030-89137-4_6
• 发表时间: 2021
• 期刊: Science of Cyber Security. SciSec 2021. Lecture Notes in Computer Science
• 作者: Charlton, J.;Du, P.;Xu, S.
• 通讯作者: Xu, S.

Robust feature screening procedures for single and mixed types of data

• DOI: 10.1080/00949655.2020.1719104
• 发表时间: 2020-02
• 期刊: Journal of Statistical Computation and Simulation
• 影响因子: 1.2
• 作者: Jinhui Sun;Pang Du;Hongyu Miao;Hua Liang