Adaptive CUSUM charts for monitoring linear drifts in Poisson rates

Feng He; Lianjie Shu; Kwok-Leung Tsui

doi:10.1016/j.ijpe.2013.11.004

Adaptive CUSUM charts for monitoring linear drifts in Poisson rates

Feng He
, Lianjie Shu^*
, Kwok-Leung Tsui

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

12 Citations (Scopus)

Abstract

There has been relatively less attention paid to the monitoring of linear drifts in Poisson rates. Although the conventional CUSUM charts can be used for monitoring linear drifts in Poisson rates, they often rely on the assumption of a known parameter. To get rid of this assumption, this paper develops adaptive CUSUM (ACUSUM) charts for monitoring drifts in Poisson rates. The basic idea is to first estimate the current Poisson mean level and then to dynamically update the likelihood ratio in the CUSUM chart based on the estimated mean. Three different mean estimators based on the exponentially weighted moving average (EWMA) schemes are discussed. The comparison results are shown to favor the ACUSUM chart, especially at small drifts. © 2013 Elsevier B.V.

Original language	English
Pages (from-to)	14-20
Journal	International Journal of Production Economics
Volume	148
Online published	15 Nov 2013
DOIs	https://doi.org/10.1016/j.ijpe.2013.11.004
Publication status	Published - Feb 2014

Research Keywords

Change-point detection
Linear drift
Poisson distribution
Statistical process control

RGC Funding Information

RGC-funded

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10.1016/j.ijpe.2013.11.004

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@article{9701b31345d54b6db009a637b36f91d8,

title = "Adaptive CUSUM charts for monitoring linear drifts in Poisson rates",

abstract = "There has been relatively less attention paid to the monitoring of linear drifts in Poisson rates. Although the conventional CUSUM charts can be used for monitoring linear drifts in Poisson rates, they often rely on the assumption of a known parameter. To get rid of this assumption, this paper develops adaptive CUSUM (ACUSUM) charts for monitoring drifts in Poisson rates. The basic idea is to first estimate the current Poisson mean level and then to dynamically update the likelihood ratio in the CUSUM chart based on the estimated mean. Three different mean estimators based on the exponentially weighted moving average (EWMA) schemes are discussed. The comparison results are shown to favor the ACUSUM chart, especially at small drifts. {\textcopyright} 2013 Elsevier B.V.",

keywords = "Change-point detection, Linear drift, Poisson distribution, Statistical process control",

author = "Feng He and Lianjie Shu and Kwok-Leung Tsui",

year = "2014",

month = feb,

doi = "10.1016/j.ijpe.2013.11.004",

language = "English",

volume = "148",

pages = "14--20",

journal = "International Journal of Production Economics",

issn = "0925-5273",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Adaptive CUSUM charts for monitoring linear drifts in Poisson rates

AU - He, Feng

AU - Shu, Lianjie

AU - Tsui, Kwok-Leung

PY - 2014/2

Y1 - 2014/2

N2 - There has been relatively less attention paid to the monitoring of linear drifts in Poisson rates. Although the conventional CUSUM charts can be used for monitoring linear drifts in Poisson rates, they often rely on the assumption of a known parameter. To get rid of this assumption, this paper develops adaptive CUSUM (ACUSUM) charts for monitoring drifts in Poisson rates. The basic idea is to first estimate the current Poisson mean level and then to dynamically update the likelihood ratio in the CUSUM chart based on the estimated mean. Three different mean estimators based on the exponentially weighted moving average (EWMA) schemes are discussed. The comparison results are shown to favor the ACUSUM chart, especially at small drifts. © 2013 Elsevier B.V.

AB - There has been relatively less attention paid to the monitoring of linear drifts in Poisson rates. Although the conventional CUSUM charts can be used for monitoring linear drifts in Poisson rates, they often rely on the assumption of a known parameter. To get rid of this assumption, this paper develops adaptive CUSUM (ACUSUM) charts for monitoring drifts in Poisson rates. The basic idea is to first estimate the current Poisson mean level and then to dynamically update the likelihood ratio in the CUSUM chart based on the estimated mean. Three different mean estimators based on the exponentially weighted moving average (EWMA) schemes are discussed. The comparison results are shown to favor the ACUSUM chart, especially at small drifts. © 2013 Elsevier B.V.

KW - Change-point detection

KW - Linear drift

KW - Poisson distribution

KW - Statistical process control

UR - https://www.scopus.com/pages/publications/84890840834

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84890840834&origin=recordpage

U2 - 10.1016/j.ijpe.2013.11.004

DO - 10.1016/j.ijpe.2013.11.004

M3 - RGC 21 - Publication in refereed journal

SN - 0925-5273

VL - 148

SP - 14

EP - 20

JO - International Journal of Production Economics

JF - International Journal of Production Economics

ER -

Adaptive CUSUM charts for monitoring linear drifts in Poisson rates

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Adaptive CUSUM charts for monitoring linear drifts in Poisson rates

Abstract

Research Keywords

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CRF: Syndromic Surveillance and Modeling for Infectious Diseases

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