9 Citations (Scopus)

Abstract

This paper introduces a one-sided multivariate exponentially weighted moving average (MEWMA) chart for detecting an increase in a process mean. The control limits are based on the multivariate Poisson distribution and have applications for both industrial processes and public health data. The statistical performance of the proposed MEWMA is examined using run length distributions and is also compared to the traditional MEWMA based on normal-theory limits. Two out-of-control scenarios are of interest: 1) detecting a single point plotting beyond the control limits 2) a run of two or more points in a row.

Original languageEnglish (US)
Pages (from-to)15-42
Number of pages28
JournalInternational Journal of Data Analysis Techniques and Strategies
Volume6
Issue number1
DOIs
StatePublished - 2014

Fingerprint

Exponentially Weighted Moving Average Control Chart
Exponentially Weighted Moving Average
Siméon Denis Poisson
Poisson distribution
Process Mean
Run Length
Multivariate Distribution
Public Health
Public health
Chart
Scenarios
Control charts
Exponentially weighted moving average

Keywords

  • Multivariate Poisson Distribution
  • One-Sided Mewma Control Chart

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Applied Mathematics

Cite this

A one-sided MEWMA control chart for Poisson-distributed data. / Laungrungrong, Busaba; Borror, Connie M.; Montgomery, Douglas.

In: International Journal of Data Analysis Techniques and Strategies, Vol. 6, No. 1, 2014, p. 15-42.

Research output: Contribution to journalArticle

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AB - This paper introduces a one-sided multivariate exponentially weighted moving average (MEWMA) chart for detecting an increase in a process mean. The control limits are based on the multivariate Poisson distribution and have applications for both industrial processes and public health data. The statistical performance of the proposed MEWMA is examined using run length distributions and is also compared to the traditional MEWMA based on normal-theory limits. Two out-of-control scenarios are of interest: 1) detecting a single point plotting beyond the control limits 2) a run of two or more points in a row.

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