报告题目：A multiple change-point detection procedure in a linearmodel

报告华体会（中国）官方：2016年12月29日上午10：30

报告地点：华体会网页入口504室

报告人:史晓平Assistant Professor（Thompson Rivers University)

报告人简介：史晓平，2002年毕业于重庆大学应用数学本科专业，而后加入合肥工业大学担任助教职务，2008年获得中国科学技术大学概率统计硕士学位，2011年获得加拿大约克大学 统计博士学位，随后在 多伦多大学从事博士后研究，先后在约克大学和圣弗朗西斯·格扎维埃大学任教，2016年加入汤姆森河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。迄今为止,在国际著名的统计学期刊《Proceeding of National Academy Science》、《Computational Statistics & Data Analysis》、《Statistica Sinica》、《Computational Statistics and Data Analysis》、《Statistics and Computing》、《Canadian Journal of Statistics》、《Journal of Multivariate Analysis》、《Statistics and Probability Letters》等上发表高水平SCI学术论文近20篇。

目前正承担加拿大政府和学校的科研基金项目多项。

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