SEGMENTATION AND ESTIMATION OF CHANGE-POINT MODELS: FALSE POSITIVE CONTROL AND CONFIDENCE REGIONS

• 成果类型: Article
• 署名作者: Fang, Xiao; Li, Jian; Siegmund, David
• 署名单位: Chinese University of Hong Kong; Adobe Systems Inc.; Stanford University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/19-AOS1861
• 发表日期: 2020
• 页码: 1615-1647
• 关键词: copy-number; binary segmentation; tail probabilities; algorithms; maxima; tests

摘要

To segment a sequence of independent random variables at an unknown number of change-points, we introduce new procedures that are based on thresholding the likelihood ratio statistic, and give approximations for the probability of a false positive error when there are no change-points. We also study confidence regions based on the likelihood ratio statistic for the change-points and joint confidence regions for the change-points and the parameter values. Applications to segment array CGH data are discussed.