distM1

<p>This is the variable name for a distance matrix in the "Example" data. It is constructed from a sequence of 40 observations of dimension 10. The first 20 observations are considered historical observations. There is a change in mean at t = 10.</p>

Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.  See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.

Hao Chen

distM1: An distance matrix constructed from L2 distance

Description

Arguments