First-order optimal sequential subspace change-point detection
Statistics Theory
2018-06-29 v1 Statistics Theory
Abstract
We consider the sequential change-point detection problem of detecting changes that are characterized by a subspace structure. Such changes are frequent in high-dimensional streaming data altering the form of the corresponding covariance matrix. In this work we present a Subspace-CUSUM procedure and demonstrate its first-order asymptotic optimality properties for the case where the subspace structure is unknown and needs to be simultaneously estimated. To achieve this goal we develop a suitable analytical methodology that includes a proper parameter optimization for the proposed detection scheme. Numerical simulations corroborate our theoretical findings.
Cite
@article{arxiv.1806.10760,
title = {First-order optimal sequential subspace change-point detection},
author = {Liyan Xie and George V. Moustakides and Yao Xie},
journal= {arXiv preprint arXiv:1806.10760},
year = {2018}
}