线性模型中的多变点检测程序
A multiple change-point detection procedure in a linear model
报告时间：2016年12月19日（周一）下午15:00-14:00
报告地点：开云·电竞(中国)官方网站二楼会议室
报告人：史晓平，2002年毕业于重庆大学应用数学本科专业，2008年获得中国科学技术大学概率统计硕士学位， 随后赴加拿大约克大学攻读统计博士学位并于2011年获得博士学位，紧接着在多伦多大学从事博士后研究，随后分别在约克大学和圣弗朗西斯·格扎维埃大学任教，2016年加入坎卢普斯大学至今担任助理教授职务，主要从事分布的鞍点近似，复合似然推断，变量选择，基于图论方法的变点检测，以及图像的去噪。
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.