Advanced multivariate cumulative sum control charts based on principal component method with application

2021 ◽  
Author(s):  
Muhammad Riaz ◽  
Babar Zaman ◽  
Rashid Mehmood ◽  
Nasir Abbas ◽  
Mu'azu Abujiya
Keyword(s):  
Control Charts ◽  
Principal Component ◽  
Cumulative Sum ◽  
Principal Component Method ◽  
Component Method ◽  
Cumulative Sum Control

Cumulative sum control charts for monitoring geometrically inflated Poisson processes: An application to infectious disease counts data

2016 ◽  
Vol 27 (2) ◽  
pp. 622-641 ◽  
Author(s):  
Athanasios C Rakitzis ◽  
Philippe Castagliola ◽  
Petros E Maravelakis
Keyword(s):  
Control Charts ◽  
Statistical Design ◽  
Poisson Processes ◽  
Cumulative Sum ◽  
The Usa ◽  
Control Situations ◽  
Zero Values ◽  
Cumulative Sum Control ◽  
Two Parameter ◽  
Excessive Number

In this work, we study upper-sided cumulative sum control charts that are suitable for monitoring geometrically inflated Poisson processes. We assume that a process is properly described by a two-parameter extension of the zero-inflated Poisson distribution, which can be used for modeling count data with an excessive number of zero and non-zero values. Two different upper-sided cumulative sum-type schemes are considered, both suitable for the detection of increasing shifts in the average of the process. Aspects of their statistical design are discussed and their performance is compared under various out-of-control situations. Changes in both parameters of the process are considered. Finally, the monitoring of the monthly cases of poliomyelitis in the USA is given as an illustrative example.

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model

2017 ◽  
Vol 27 (9) ◽  
pp. 2859-2871 ◽  
Author(s):  
Orlando Yesid Esparza Albarracin ◽  
Airlane Pereira Alencar ◽  
Linda Lee Ho
Keyword(s):  
Control Charts ◽  
Negative Binomial ◽  
Moving Average ◽  
Health Surveillance ◽  
Autoregressive Moving Average ◽  
Cumulative Sum ◽  
Moving Average Models ◽  
Cumulative Sum Control ◽  
The Impact ◽  
Autoregressive Moving Average Models

Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals. In practical health surveillance, count series can be modeled by Poisson or Negative Binomial regression, this last to control overdispersion. To include serial correlations, generalized autoregressive moving average models are proposed. The main contribution of the current article is to measure the impact, in terms of average run length on the performance of cumulative sum charts when the serial correlation is neglected in the regression model. Different statistics based on transformations, the deviance residual, and the likelihood ratio are used to build cumulative sum control charts to monitor counts with time varying means, including trend and seasonal effects. The monitoring of the weekly number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current method.

scholarly journals Implementation of multivariate cumulative sum control charts in mastitis and lameness monitoring

2013 ◽  
Vol 96 (9) ◽  
pp. 5723-5733 ◽  
Author(s):  
Bettina Miekley ◽  
Eckhard Stamer ◽  
Imke Traulsen ◽  
Joachim Krieter
Keyword(s):  
Control Charts ◽  
Cumulative Sum ◽  
Cumulative Sum Control

scholarly journals Analysing the Data on Incomes in the Regional Context by the Principal Component Method

2019 ◽  
Vol 15 (2) ◽  
pp. 601-617
Author(s):  
B G. Ilyasov ◽  
E.A. Makarova ◽  
E.S. Zakieva ◽  
E.S. Gizdatullina
Keyword(s):  
Principal Component ◽  
Principal Component Method ◽  
Regional Context ◽  
Component Method
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