Signal interpretation in Hotelling’sT2control chart for compositional data

Vives‐Mestres, Marina; Daunis‐i‐Estadella, Pepus; Fernández, Josep Antoni Martín

doi:10.1080/0740817x.2015.1125042

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…Vives-Mestres, Daunis-i-Estadella ve Martin-Fernandez (2015) tarafından yapılan çalışmada, compositional veriler için compositional T² kontrol grafiği önerilmiştir. Söz konusu grafiğin oluşturulmasında Van den Boogaart, Tolosana ve Bren (2014) tarafından oluşturulan R compositions paketi kullanılmıştır.…”

Section: Tablo-4 R Programı Ve Kalite Kontrol Grafikleri İle İlgili Makaleler Listesiunclassified

Kalite Kontrol Grafiklerinde R Programlama Dilinin Kullanımı İle İlgili İçerik Analizi

Orçanlı

2019

OPUS Uluslararası Toplum Araştırmaları Dergisi

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Although there are various methods that help improve quality in statistical process control, quality control charts are considered to be the most important methods available. The superiority of these charts compared to other methods is that they are easy to use, to design and visually understandable. Observations related to the quality characteristics determined in the quality control charts are generally followed by the creation of package programs such as SPSS, MINITAB, STATA or programming languages such as MATLAB, C++. Recently, however, it has been determined that the use of R programs in the creation of quality control charts, the detection of uncontrolled signals in these charts, in the diagnosis of errors and similar areas is rapidly becoming widespread and a lot of studies are conducted in foreign literature. In this context, it was aimed to make a research about the use of R program in quality control charts. In this way, it is considered that this study will contribute to the quality control employees and the domestic and foreign literature. In this study, four books and thirty-six articles were examined according to content analysis method according to seven criteria. As a result of the study; (1) in the books and articles examined in relation to the use of the R program in control charts, theoretical issues are emphasized, applied issues are in the minority, (2) most of the R packages used in the design of quality control charts is qcc package (3) mainly used control graphs are multivariate control graphs in theory development and applications, and most commonly used control charts were Hotelling T², MCUSUM and MEWMA control charts, and (4) control charts were measured using ARL. (5) mainly used artificial data in applications with control charts, (6) Monte Carlo simulation method is preferred in simulation studies, (7) it is concluded that a wide variety of control charts suggested for various situations are included in the literature.

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Section: Tablo-4 R Programı Ve Kalite Kontrol Grafikleri İle İlgili Makaleler Listesiunclassified

Kalite Kontrol Grafiklerinde R Programlama Dilinin Kullanımı İle İlgili İçerik Analizi

Orçanlı

2019

OPUS Uluslararası Toplum Araştırmaları Dergisi

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“…In the customer satisfaction context, they could mean either hot items that should be improved or best features. The specific cause can be deduced using signal interpretation methods of the

T_{C}^{2}

…”

Section: Compositional T2 Control Chartmentioning

confidence: 99%

“…When we apply the general signal interpretation method, we get to the conclusion that the cause of the signal in q41 is mainly due to the log ratio ( n 4 n mis )/( n 1 n 5 ) and in q66 due to the log ratio n mis / n 3 . This is consistent to what is seen in raw data shown in Table when compared with the centre of the dataset: E[ n q ] = (17,12,24,63,94,43,13) in frequency units (also shown in % in Table ).…”

Section: Compositional T2 Control Chartmentioning

confidence: 99%

Compositional Data Methods in Customer Survey Analysis

Vives‐Mestres

Fernández

Kenett

2016

Quality & Reliability Eng

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Customer satisfaction is usually measured by questionnaires with statements scored on an anchored scale. Responses to such surveys consist of compositional data (CoDa) by considering the frequency distribution of ratings across questions or respondents. By CoDa, we mean vectors whose elements contain relative information, that is, its total sum is not informative. In this paper, we explore the contribution of CoDa methodology to the analysis of customer satisfaction surveys. Compositional methods are based on the principle of working on coordinates, that is, values obtained by the logarithm of ratios of the parts in a composition. We present common compositional tools such as descriptive statistics, TC2 control chart and principal component analysis (among others), and show an example of application to the annual customer satisfaction survey of the ABC Company. We highlight the advantage of the compositional approach in dealing with non response, which turns into a difficulty when dealing with zeros. We finally underline the pros and cons of the proposed analysis. Copyright © 2016 John Wiley & Sons, Ltd.

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“…They compared the new method with the classical T 2 control chart (after having deleted one variable), in terms of in‐control average run length ( A R L ). Very recently, Vives‐Mestres et al proposed 2 methods for the interpretation of the out‐of‐control signals of the individual

T_{C}^{2}

control chart for more than 3 components. In the very particular case where the ratio of population means of a bivariate normal distribution has to be monitored over time without taking into account the whole composition, several control charts have been proposed and investigated by Celano et al, Celano and Castagliola, Celano and Castagliola, Tran et al, Tran et al, Tran et al, and Tran …”

Section: Introductionmentioning

confidence: 99%

Monitoring compositional data using multivariate exponentially weighted moving average scheme

Tran

Castagliola

Celano

et al. 2018

Quality & Reliability Eng

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Recently, the monitoring of compositional data by means of control charts has been investigated in the statistical process control literature. In this article, we develop a Phase II multivariate exponentially weighted moving average control chart, for the continuous surveillance of compositional data based on a transformation into coordinate representation. We use a Markov chain approximation to determine the performance of the proposed multivariate control chart. The optimal multivariate exponentially weighted moving average smoothing constants, control limits, and out‐of‐control average run lengths have been computed for different combinations of the in‐control average run lengths and the number of variables. Several tables are presented and enumerated to show the statistical performance of the proposed control chart. An example illustrates the use of this chart on an industrial problem from a plant in Europe.

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Signal interpretation in Hotelling’sT²control chart for compositional data

Cited by 19 publications

References 15 publications

Kalite Kontrol Grafiklerinde R Programlama Dilinin Kullanımı İle İlgili İçerik Analizi

Kalite Kontrol Grafiklerinde R Programlama Dilinin Kullanımı İle İlgili İçerik Analizi

Compositional Data Methods in Customer Survey Analysis

Monitoring compositional data using multivariate exponentially weighted moving average scheme

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