Document Type

Report

Date

3-1-2010

Embargo Period

9-1-2010

Keywords

Average Run Length, Change Detection, Moving Average, Filtered Derivative, Control Charts

Language

English

Disciplines

Computer Engineering | Electrical and Computer Engineering

Description/Abstract

Among the various procedures used to detect potential changes in a stochastic process the moving sum algorithms are very popular due to their intuitive appeal and good statistical performance. One of the important design parameters of a change detection algorithm is the expected interval between false positives, also known as the average run length (ARL). Computation of the ARL usually involves numerical procedures but in some cases it can be approximated using a series involving multivariate probabilities. In this paper, we present an analysis of this series approach by providing sufficient conditions for convergence and derive an error bound. Using simulation studies, we show that the series approach is applicable to moving average and filtered derivative algorithms. For moving average algorithms, we compare our results with previously known bounds. We use two special cases to illustrate our observations.

Additional Information

SYR-EECS-2010-01

Source

local input

 
 

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