Asymptotic distribution-free change-point detection for multivariate and non-Euclidean data
多元与非欧氏数据的渐近分布无关变点检测
We consider the testing and estimation of change-points, locations where the distribution abruptly changes, in a sequence of multivariate or non-Euclidean observations. We study a nonparametric framework that utilizes similarity information among observations, which can be applied to various data types as long as an informative similarity measure on the sample space can be defined. The existing approach along this line has low power and/or biased estimates for change-points…