On Robustness of the Shiryaev-Roberts Procedure for Quickest Change-Point Detection under Parameter Misspecification in the Post-Change Distribution
Wenyu Du, Aleksey S. Polunchenko, Grigory Sokolov

TL;DR
This paper investigates the robustness of the Shiryaev-Roberts change-point detection procedure when the post-change distribution parameter is misspecified, using a Gaussian case study to quantify the impact on detection delay.
Contribution
It provides a quantitative robustness analysis of the SR procedure under parameter misspecification, offering insights for practical implementation and future theoretical development.
Findings
SR procedure's robustness varies with change contrast and false alarm level.
Misspecification increases detection delay, especially with low contrast changes.
The study offers a framework for designing more robust change detection methods.
Abstract
The gist of the quickest change-point detection problem is to detect the presence of a change in the statistical behavior of a series of sequentially made observations, and do so in an optimal detection-speed-vs.-"false-positive"-risk manner. When optimality is understood either in the generalized Bayesian sense or as defined in Shiryaev's multi-cyclic setup, the so-called Shiryaev-Roberts (SR) detection procedure is known to be the "best one can do", provided, however, that the observations' pre- and post-change distributions are both fully specified. We consider a more realistic setup, viz. one where the post-change distribution is assumed known only up to a parameter, so that the latter may be "misspecified". The question of interest is the sensitivity (or robustness) of the otherwise "best" SR procedure with respect to a possible misspecification of the post-change distribution…
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Taxonomy
TopicsStatistical Methods and Inference · Advanced Statistical Process Monitoring
