报告时间：2019年5月29日（星期三）10:30

报告地点：翡翠科教楼B座1710

工作单位：汤姆森河大学

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， 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，博士毕业后在加拿大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，以及图像的去噪等研究工作。研究成果主要发表在PNAS, Canadian Journal of Statistics, Statistica Sinica, Statistics and Computing, 中国科学，等。

报告简介：

The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees’ flower visits is illustrated.