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Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data
时间  Datetime
2019-05-27 10:00 — 11:00 
地点  Venue
Middle Lecture Room(703)
报告人  Speaker
Prof. Shi Xiaoping
单位  Affiliation
Department of Mathematics and Statistics, Thompson Rivers University
邀请人  Host
Cheng Wang
报告摘要  Abstract

摘要: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.

报告人介绍:史晓平博士,2011年博士毕业于加拿大约克大学,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。研究成果主要发表在PNAS, Canadian Journal of Statistics, Statistica Sinica, Statistics and Computing, 中国科学等.