{"title":"Robust selection of the number of change-points via FDR control","authors":"Hui Chen , Chengde Qian , Qin Zhou","doi":"10.1016/j.csda.2025.108272","DOIUrl":null,"url":null,"abstract":"<div><div>Robust quantification of uncertainty regarding the number of change-points presents a significant challenge in data analysis, particularly when employing false discovery rate (FDR) control techniques. Emphasizing the detection of genuine signals while controlling false positives is crucial, especially for identifying shifts in location parameters within flexible distributions. Traditional parametric methods often exhibit sensitivity to outliers and heavy-tailed data. Addressing this limitation, a robust method accommodating diverse data structures is proposed. The approach constructs component-wise sign-based statistics. Leveraging the global symmetry inherent in these statistics enables the derivation of data-driven thresholds suitable for multiple testing scenarios. Method development occurs within the framework of U-statistics, which naturally encompasses existing cumulative sum-based procedures. Theoretical guarantees establish FDR control for the component-wise sign-based method under mild assumptions. Demonstrations of effectiveness utilize simulations with synthetic data and analyses of real data.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"214 ","pages":"Article 108272"},"PeriodicalIF":1.6000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics & Data Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167947325001483","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Robust quantification of uncertainty regarding the number of change-points presents a significant challenge in data analysis, particularly when employing false discovery rate (FDR) control techniques. Emphasizing the detection of genuine signals while controlling false positives is crucial, especially for identifying shifts in location parameters within flexible distributions. Traditional parametric methods often exhibit sensitivity to outliers and heavy-tailed data. Addressing this limitation, a robust method accommodating diverse data structures is proposed. The approach constructs component-wise sign-based statistics. Leveraging the global symmetry inherent in these statistics enables the derivation of data-driven thresholds suitable for multiple testing scenarios. Method development occurs within the framework of U-statistics, which naturally encompasses existing cumulative sum-based procedures. Theoretical guarantees establish FDR control for the component-wise sign-based method under mild assumptions. Demonstrations of effectiveness utilize simulations with synthetic data and analyses of real data.
期刊介绍:
Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas:
I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.
II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.
[...]
III) Special Applications - [...]
IV) Annals of Statistical Data Science [...]