An effective nonparametric quickest detection procedure based on Q-Q distance

Abstract

Quickest detection schemes are geared toward detecting a change in the state of a data stream or a real-time process. Classical quickest detection schemes invariably assume knowledge of the pre-change and post-change distributions that may not be available in many applications. In this paper, we present a distribution free nonparametric quickest detection procedure based on a novel distance measure, referred to as the Q-Q distance calculated from the Q-Q plot, for detection of distribution changes. Through experimental study, we show that the Q-Q distance-based detection procedure presents comparable or better performance compared to classical parametric and other nonparametric procedures. The proposed procedure is most effective when detecting small changes.