题目：A Fast Algorithm for Multiple Change Point Detection

报告人：史晓平 教授 (Thompson Rivers University, Canada)

时间：12月20日（星期二） 下午4:00-5:00

地点：致远楼105室

史晓平，加拿大Thompson Rivers University大学助理教授。2011年获得加拿大约克大学统计博士学位，而后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。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。迄今为止已在Proceedings of the National Academy of Sciences Statistica Sinica、Computational Statistics，Journal of Mathematical Analysis and Applications等国际期刊上发表学术论文二十余篇。

摘要：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.

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