線性模型中的多變點檢測程序
A multiple change-point detection procedure in a linear model
報告時間:2016年12月19日(周一)下午15:00-14:00
報告地點:必一体育平台二樓會議室
報告人:史曉平,2002年畢業於重慶大學應用數學本科專業,2008年獲得中國科學技術大學概率統計碩士學位, 隨後赴加拿大約克大學攻讀統計博士學位並於2011年獲得博士學位,緊接著在多倫多大學從事博士後研究,隨後分別在約克大學和聖弗朗西斯·格紮維埃大學任教,2016年加入坎盧普斯大學至今擔任助理教授職務,主要從事分布的鞍點近似,復合似然推斷,變量選擇,基於圖論方法的變點檢測,以及圖像的去噪。
Abstract:A change point refers to a location or time at which observations or data obey two different models: before and after. These studies of change-point problems have found applications in a wide range of areas, including quality control, finance, environmetrics, medicine, genetics and geography. We propose a procedure for detecting multiple change-points in a mean-shift model. We first convert the change-point problem into a variable selection problem by partitioning the data sequence into several segments. Then, we apply a modified variance inflation factor regression algorithm to each segment in sequential order. When a segment that is suspected of containing a change-point is found, we use a weighted cumulative sum to test if there is indeed a change-point in this segment. Two real data examples including a barcode image and a genetic dataset are illustrated for change-point detection.