EWMA control charts for monitoring binary processes with applications to medical diagnosis data

Weiß, Christian; Atzmüller, Martin

doi:10.1002/qre.1098

Cited by 16 publications

(5 citation statements)

References 34 publications

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“…The starting value 𝑧 0 = 𝜇 0 when the process target is known or 𝑧 0 = x when using the average of an initial dataset. The central line and control limits of the EWMA chart are given by the following formula The construction of the individual two-parameter Lindley control charts is going to be done based on the EWMA control charts (13) using the skewness correction as in Chan and Cui, 22 since the distribution of concern is asymmetric and, as also mentioned in Weiß and Atzmüller 33 , this is an easily applied method for taking the distribution's skewness into consideration and leads to a better ARL performance of the resulting control chart. In the next section, where we deal with the performance investigation of the constructed control chart, we will further demonstrate the need for this adjustment considering the asymmetry of the distribution and the improvement in the performance of the chart when using the skewness correction contrary to not using it but using the traditionally used symmetric EWMA control limits instead.…”

Section: Construction Of the Ewma Control Charts For Individual Obser...mentioning

confidence: 99%

Probability‐type, Shewhart‐type and EWMA control charts for individual observations from the two‐parameter Lindley distribution

Demertzi,

Psarakis

2023

Quality & Reliability Eng

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Lindley distribution is a continuous distribution with various applications some of which are in medicine, genetics, epidemiology, biology, finance and actuarial sciences, ecology, meteorology, sociology, demography, agriculture, hydrology, geosciences, reliability and engineering, life testing and survival analysis, airborne systems and communications, environmental studies and modeling and describing of human mistakes, strikes, accidents, behavioural and emotional or IQ test scores and waiting times of customers in queues until service, etc. Due to its variety of applications, it appears to be important that control charts for detected shifts in a process should be constructed under the assumption that the quality characteristic of interest follows a Lindley‐related distribution. Here, we construct probability‐type, Shewhart‐type and EWMA control charts (and deal with the optimal choice of its parameters) for individual observations from the two‐parameter Lindley distribution, investigate and compare their performance and illustrate them using examples with both simulated and real data. The whole analysis reveals the superiority of using skewness correction for the construction of the control charts against not using it.

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Section: Construction Of the Ewma Control Charts For Individual Obser...mentioning

confidence: 99%

Probability‐type, Shewhart‐type and EWMA control charts for individual observations from the two‐parameter Lindley distribution

Demertzi,

Psarakis

2023

Quality & Reliability Eng

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“…The problem of monitoring a Bernoulli process and the respective ‘success’ probability p has been studied by several researchers. There are several works on CUSUM and EWMA charts for monitoring proportions; see Aytaçoğlu and Woodall, 13 Daryabari et al, 14 Joner Jr et al, 15 Neuburger et al, 16 Reynolds and Stoumbos, 17 Rossi et al, 18 Sego et al, 19 Spliid 20 and Weiß and Atzmüller 21 and references therein. Szarka and Woodall 22 provided an extensive review that covers a wide variety of methods.…”

Section: Introductionmentioning

confidence: 99%

Exponentially weighed moving average charts for monitoring zero-inflated proportions with applications in health care

Maravelakis

Rakitzis

Castagliola

2022

Stat Methods Med Res

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In the context of public health surveillance, the aim is to monitor the occurrence of health-related events. Among them, statistical process monitoring focuses very often on the monitoring of rates and proportions (i.e. values in [Formula: see text]) such as the proportion of patients with a specific disease. A popular control chart that is able to detect quickly small to moderate shifts in process parameters is the exponentially weighed moving average control chart. There are various models that are used to describe values in [Formula: see text]. However, especially in the case of rare health events, zero values occur very frequently which, for example, denote the absence of the disease. In this paper, we study the performance and the statistical design of exponentially weighed moving average control charts for monitoring proportions that arise in a health-related framework. The proposed chart is based on the zero-inflated Beta distribution, a mixed (discrete-continuous) distribution, suitable for modelling data in [Formula: see text]. We use a Markov chain method to study the run length distribution of the exponentially weighed moving average chart. Also, we investigate the statistical design as well as the performance of the proposed charts. Comparisons with a Shewhart-type chart are also given. Finally, we provide an example for the practical implementation of the proposed charts.

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“…Among different SPC tools, practitioners and researchers generally prefer a quality control chart due to an efficient methodology and wide applications. For instance, the application of control charts can be seen in analytical measurements [4], medical sciences [5], industrial sciences [6], agricultural sciences [7], and environmental sciences [8,9]. Likewise, applications of control charts can be seen for monitoring cyclone impact, fire frequency and fisheries management [10].…”

Section: Introductionmentioning

confidence: 99%

Application of Quality Control Charts for Early Detection of Flood Hazards

Mehmood¹,

Lee²,

Baqar³

et al. 2020

Pol. J. Environ. Stud.

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Flooding is a serious and devastating hazard that many countries face regularly. Timely detection of these changes can help us manage rainwater and hence flooding. Numerous statistical tools are available for the early detection of these changes. In this regard, application of control charts is an effective choice toward monitoring natural events. In this study, we used self-proposed and existing control charts as a means of early detection of changes in the rainfall data of Pakistan as a case study. The proposed methodology covered two aspects: one to enhance the ability of Shewhart control charts toward detection of small or moderate changes in the behavior of natural events, and another to offer skewness correction based on individual control chart under runs rules for managing these natural events-especially when collected data follows unknown skewed distribution. Results elucidate that control charts structures were efficient in early detection and prediction of floods during 2010. Our results are in accordance with the theoretical results of existing studies, and we propose similar methods to be used for meteorological purposes.

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EWMA control charts for monitoring binary processes with applications to medical diagnosis data

Cited by 16 publications

References 34 publications

Probability‐type, Shewhart‐type and EWMA control charts for individual observations from the two‐parameter Lindley distribution

Probability‐type, Shewhart‐type and EWMA control charts for individual observations from the two‐parameter Lindley distribution

Exponentially weighed moving average charts for monitoring zero-inflated proportions with applications in health care

Application of Quality Control Charts for Early Detection of Flood Hazards

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