A Binary Control Chart to Detect Small Jumps

TL;DR

This paper introduces a simple, recent-observation-based binary control chart designed to detect very small shifts in binary data, demonstrating superior performance in simulations and applicable to dependent data.

Contribution

It proposes a new binary control chart using only the most recent observations, with theoretical support for dependent data, improving small shift detection.

Findings

Superior out-of-control average run length for small shifts

Asymptotic results valid for dependent data under martingale difference conditions

Applicable to a broad class of time series and image data

Abstract

The classic N p chart gives a signal if the number of successes in a sequence of inde- pendent binary variables exceeds a control limit. Motivated by engineering applications in industrial image processing and, to some extent, financial statistics, we study a simple modification of this chart, which uses only the most recent observations. Our aim is to construct a control chart for detecting a shift of an unknown size, allowing for an unknown distribution of the error terms. Simulation studies indicate that the proposed chart is su- perior in terms of out-of-control average run length, when one is interest in the detection of very small shifts. We provide a (functional) central limit theorem under a change-point model with local alternatives which explains that unexpected and interesting behavior. Since real observations are often not independent, the question arises whether these re- sults still hold true for the dependent case. Indeed, our asymptotic results work under the fairly general condition that the observations form a martingale difference array. This enlarges the applicability of our results considerably, firstly, to a large class time series models, and, secondly, to locally dependent image data, as we demonstrate by an example.