CUSUM Charts for Multivariate Monitoring and Forecasting

KWOK-LEUNG TSUI; YAJUN MEI; WEI JIANG

doi:10.1002/9780470400531.eorms0215

CUSUM Charts for Multivariate Monitoring and Forecasting

KWOK-LEUNG TSUI, YAJUN MEI, WEI JIANG

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

2 Citations (Scopus)

Abstract

Based on likelihood ratio tests, this article presents CUSUM methods for multivariate monitoring and forecasting. Several recent applications including industrial process control, disease surveillance, sensor networks, and financial and economic analysis are discussed.

Original language	English
Journal	Wiley Encyclopedia of Operations Research and Management Science
DOIs	https://doi.org/10.1002/9780470400531.eorms0215
Publication status	Published - 2010
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research Keywords

CUSUM statistic
spatiotemporal surveillance
mean shifts
sample variance
SPC

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10.1002/9780470400531.eorms0215

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@article{918b27c33f594bd180d143f70e4d8e0c,

title = "CUSUM Charts for Multivariate Monitoring and Forecasting",

abstract = "Based on likelihood ratio tests, this article presents CUSUM methods for multivariate monitoring and forecasting. Several recent applications including industrial process control, disease surveillance, sensor networks, and financial and economic analysis are discussed.",

keywords = "CUSUM statistic, spatiotemporal surveillance, mean shifts, sample variance, SPC",

author = "KWOK-LEUNG TSUI and YAJUN MEI and WEI JIANG",

year = "2010",

doi = "10.1002/9780470400531.eorms0215",

language = "English",

}

TY - JOUR

T1 - CUSUM Charts for Multivariate Monitoring and Forecasting

AU - TSUI, KWOK-LEUNG

AU - MEI, YAJUN

AU - JIANG, WEI

PY - 2010

Y1 - 2010

N2 - Based on likelihood ratio tests, this article presents CUSUM methods for multivariate monitoring and forecasting. Several recent applications including industrial process control, disease surveillance, sensor networks, and financial and economic analysis are discussed.

AB - Based on likelihood ratio tests, this article presents CUSUM methods for multivariate monitoring and forecasting. Several recent applications including industrial process control, disease surveillance, sensor networks, and financial and economic analysis are discussed.

KW - CUSUM statistic

KW - spatiotemporal surveillance

KW - mean shifts

KW - sample variance

KW - SPC

U2 - 10.1002/9780470400531.eorms0215

DO - 10.1002/9780470400531.eorms0215

M3 - RGC 21 - Publication in refereed journal

JO - Wiley Encyclopedia of Operations Research and Management Science

JF - Wiley Encyclopedia of Operations Research and Management Science

ER -

CUSUM Charts for Multivariate Monitoring and Forecasting

Abstract

UN SDGs

Research Keywords

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