Robust Sequential Change-Point Detection by Convex Optimization

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.

Accepted by ISIT 2017

References in corpus (3)

• Online Learning for Time Series Prediction
• Sequential change-point detection when unknown parameters are present in the pre-change distribution
• Change Detection via Affine and Quadratic Detectors

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