State-of-the-Art in Sequential Change-Point Detection
Statistics Theory
2011-09-21 v1 Statistics Theory
Abstract
We provide an overview of the state-of-the-art in the area of sequential change-point detection assuming discrete time and known pre- and post-change distributions. The overview spans over all major formulations of the underlying optimization problem, namely, Bayesian, generalized Bayesian, and minimax. We pay particular attention to the latest advances in each. Also, we link together the generalized Bayesian problem with multi-cyclic disorder detection in a stationary regime when the change occurs at a distant time horizon. We conclude with two case studies to illustrate the cutting edge of the field at work.
Keywords
Cite
@article{arxiv.1109.2938,
title = {State-of-the-Art in Sequential Change-Point Detection},
author = {Aleksey S. Polunchenko and Alexander G. Tartakovsky},
journal= {arXiv preprint arXiv:1109.2938},
year = {2011}
}
Comments
34 pages, accepted for publication in Methodology and Computing in Applied Probability