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arXiv · 1701.06952

Robust Sequential Change-Point Detection by Convex Optimization

Yang Cao·Yao Xie
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Abstract

We address the computational challenge of finding the robust sequential change-point detection procedures when the pre- and post-change distributions are not completely specified. Earlier works [veeravalli 1994] and [Unnikrishnan 2011] establish the general conditions for robust procedures which include finding a pair of least favorable distributions (LFDs). However, in the multi-dimensional setting, it is hard to find such LFDs computationally. We present a method based on convex optimization that addresses this issue when the distributions are Gaussian with unknown parameters from pre-specified uncertainty sets. We also establish theoretical properties of our robust procedures, and numerical examples demonstrate their good performance.

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Yang Cao, Yao Xie. 2017-01-24. Robust Sequential Change-Point Detection by Convex Optimization. https://doi.org/10.3390/e20020108

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