Process monitoring for multiple count data using generalized linear model-based control charts

Katina R. Skinner; Douglas Montgomery; George Runger

doi:10.1080/00207540210163964

Process monitoring for multiple count data using generalized linear model-based control charts

Katina R. Skinner, Douglas Montgomery, George Runger

Industrial, Systems and Operations Engineering

Research output: Contribution to journal › Article › peer-review

106 Scopus citations

Abstract

A procedure for monitoring multiple discrete counts is proposed. This procedure is based on the likelihood ratio statistic for Poisson counts when input variables are measurable. This statistic is the deviance residual resulting from a generalized linear model. The likelihood ratio statistic is derived and shown to be more effective than using a C chart on the raw counts.

Original language	English (US)
Pages (from-to)	1167-1180
Number of pages	14
Journal	International Journal of Production Research
Volume	41
Issue number	6
DOIs	https://doi.org/10.1080/00207540210163964
State	Published - Apr 15 2003

ASJC Scopus subject areas

Strategy and Management
Management Science and Operations Research
Industrial and Manufacturing Engineering

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10.1080/00207540210163964

Cite this

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title = "Process monitoring for multiple count data using generalized linear model-based control charts",

abstract = "A procedure for monitoring multiple discrete counts is proposed. This procedure is based on the likelihood ratio statistic for Poisson counts when input variables are measurable. This statistic is the deviance residual resulting from a generalized linear model. The likelihood ratio statistic is derived and shown to be more effective than using a C chart on the raw counts.",

author = "Skinner, {Katina R.} and Douglas Montgomery and George Runger",

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AU - Montgomery, Douglas

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AB - A procedure for monitoring multiple discrete counts is proposed. This procedure is based on the likelihood ratio statistic for Poisson counts when input variables are measurable. This statistic is the deviance residual resulting from a generalized linear model. The likelihood ratio statistic is derived and shown to be more effective than using a C chart on the raw counts.

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Process monitoring for multiple count data using generalized linear model-based control charts

Abstract

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