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.