On robust stopping times for detecting changes in distribution

Let X1, X2,...be independent random variables observed sequen-tially and such that X1,...,X θ−1 have a common probability density p 0, while X θ ,X θ +1,...are all distributed according to p 1 6 = p 0. It is assumed that p 0 and p 1 are known, but the time change θ ∈ Z + is unknown and the goal is to construct a stopping time τ that detects the hange-point θ as soon as possible. The existing approaches to this problem rely essentially on some a priori information about θ. For in-stance, in Bayes approaches, it is assumed that θ is a random variable with a known probability distribution. In methods related to hypothesis testing, this a priori information is hidden in the so-called verage run length. The main goal in this paper is to construct stopping times which do not make use of a priorinformation about θ, but have nearly Bayesian detection delays. More precisely, we propose stopping times solving approximately the following problem: ∆ (θ;τα) → min τα subject to α (θ;τα) ≤ α for any θ ≥ 1, where α (θ; τ ) =
P θ { τ < θ} is the false alarm probability and ∆(θ;τ) = E θ (τ−θ) + is the average detection delay, and explain why such top-ping times are robust w.r.t. ... mehr a priori information about θ.