CUSUM control charts to monitor series of Negative Binomial count data

Alencar, Airlane Pereira; Ho, Linda Lee; Albarracin, Orlando Yesid Esparza

doi:10.1177/0962280215592427

Cited by 26 publications

(54 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recentemente o CEP também tem sido aplicado em problemas de monitoramento na saúde pública (ALENCAR et al, 2015;WOODALL, 2006) e as abordagens mais recentes propõem detectar surtos de doenças infecciosas. Alencar et al (2015) propuseram a utilização de Gráficos de Controle da Soma Cumulativa (em inglês, CUSUM -Cumulative Sum) para monitorar taxas de internações devido a doenças respiratórias em pessoas com idade acima de 65 anos na cidade de São Paulo.…”

Section: Lista De Ilustraçõesunclassified

“…Alencar et al (2015) propuseram a utilização de Gráficos de Controle da Soma Cumulativa (em inglês, CUSUM -Cumulative Sum) para monitorar taxas de internações devido a doenças respiratórias em pessoas com idade acima de 65 anos na cidade de São Paulo. Foi ajustado um modelo com distribuição Binomial Negativa empregando uma função de ligação logarítmica, foi utilizado o tamanho da população como offset e um conjunto de covariáveis.…”

Section: Lista De Ilustraçõesunclassified

“…Para o mesmo conjunto de dados históricos e as mesmas transformações propostas, foi aplicado o Gráfico de Controle de Média Móvel Exponencialmente Ponderada (em inglês, EWMA -Exponentially Weighted Moving Average) para os cenários avaliados por Alencar et al (2015) e é feita uma comparação do desempenho entre os dois tipos de gráficos de controle, CUSUM e EWMA.…”

Section: Lista De Ilustraçõesunclassified

“…Em geral, os artigos constroem os gráficos de controle propondo variáveis transformadas para normalizar os dados de contagem e depois os limites são calculados baseados em distribuições Gaussianas (ROSSI, 2010;ROSSI et al, 1999). A premissa de normalidade, embora importante, usualmente não é verificada na maioria das análises (ALENCAR et al, 2015).…”

Section: Gráficos De Controle Para Monitorar Série De Contagemunclassified

“…Com o objetivo de construir gráficos de controle para monitorar série de contagem, Alencar et al (2015) consideram as seguintes premissas:…”

Section: Gráficos De Controle Para Monitorar Série De Contagemunclassified

See 4 more Smart Citations

Gráficos CUSUM e EWMA para monitorar dados de contagem com distribuição binominal negativa.

Urbieta¹

View full text Add to dashboard Cite

Section: Lista De Ilustraçõesunclassified

Section: Gráficos De Controle Para Monitorar Série De Contagemunclassified

“…Com o objetivo de construir gráficos de controle para monitorar série de contagem, Alencar et al (2015) consideram as seguintes premissas:…”

Section: Gráficos De Controle Para Monitorar Série De Contagemunclassified

See 3 more Smart Citations

Gráficos CUSUM e EWMA para monitorar dados de contagem com distribuição binominal negativa.

Urbieta¹

View full text Add to dashboard Cite

The generalized linear model‐based exponentially weighted moving average and cumulative sum charts for the monitoring of high‐quality processes

Mahmood

Xie

2021

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

In this industry 4.0 revolution, most of the manufacturing processes are equipped with the digital devices which are continuously recording the data. To monitor the quality of a manufacturing system, variable about number of conforming or nonconforming items is usually used and statistical analysis based on it is further utilized for developing the policies. In this era of sophisticated and modern technology, most of the manufacturing systems are producing near zero‐defect items. Such processes are known as high‐quality processes, and their dataset consists of excess number of zeros. Generally, the zero excess or near zero‐defect dataset is well fitted by the zero‐inflated distributions, and the zero‐inflated Poisson (ZIP) and zero‐inflated Negative Binomial (ZINB) distributions are the most common models. Most of the time, in high‐quality processes, few covariates are also measured along with defect counts. Hence, to model such processes, generalized linear model (GLM) based on ZIP and ZINB distributions are used to fit the data. In monitoring perspective, data‐based control charts are designed to monitor high‐quality datasets while the GLM‐based control charts based on the residuals of the GLM models are used to monitor a change in the mean of the zero excess datasets. In this study, we have developed memory‐type data‐based and GLM‐based control charts (i.e., exponentially weighted moving average and cumulative sum) to monitor the increasing average defect counts in a high‐quality process. Further, the proposed methods are evaluated using run‐length properties and compared with its competitive charts. Furthermore, to highlight the importance of the study, the proposed methods are implemented on a dataset concerning the number of flight delays between Atlanta and Orlando airports.

show abstract

On the improved generalized linear model‐based monitoring methods for Poisson distributed processes

Iqbal¹,

Mahmood

Nazir

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Control charts are widely used tool that provides quality inspectors with sensitive information for maintaining manufacturing process productivity. Numerous model‐based techniques have been presented in the literature to monitor industrial operations that focus on the normal response variable. However, non‐normal response results can occur as a result of quality control operations. In such cases, a new approach based on generalized linear model that provides multiple distribution options for response variables is required to achieve better results. Therefore, this study proposes GLM‐based moving average (MA) and double moving average (DMA) schemes formed on standardized residuals derived from a fitted Poisson regression model. The productivity of suggested methods and the existing exponentially weighted moving average (EWMA) scheme is explored in terms of run length attributes. The simulation outcomes revealed that moving average schemes based on standardized residuals (i.e., SR‐MA and SR‐DMA) outperform their predecessor (i.e., SR‐EWMA). Moreover, the SR‐DMA chart, with small values of span w, has proven to be more effective at detecting minor to moderate shifts in the process mean. Finally, a case study of a 3D manufacturing operation is shown to emphasize the importance of the proposed approaches.

show abstract

CUSUM control charts to monitor series of Negative Binomial count data

Cited by 26 publications

References 36 publications

Gráficos CUSUM e EWMA para monitorar dados de contagem com distribuição binominal negativa.

Gráficos CUSUM e EWMA para monitorar dados de contagem com distribuição binominal negativa.

The generalized linear model‐based exponentially weighted moving average and cumulative sum charts for the monitoring of high‐quality processes

On the improved generalized linear model‐based monitoring methods for Poisson distributed processes

Contact Info

Product

Resources

About