On efficient CUSUM-type location control charts using auxiliary information

Sanusi, Ridwan A.; Abbas, Nasir

doi:10.1080/16843703.2017.1304039

Cited by 43 publications

(25 citation statements)

References 33 publications

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“…The EQL is defined as

italicEQL = \frac{1}{δ_{\max} - δ_{\min}} \int_{{italicδ}_{italicmin}}^{{italicδ}_{italicmax}} δ^{2} italicARL ((), δ) italicdδ

where δ max and δ min are the maximum and minimum values of the shifts considered in a process, and ARL ( δ ) denotes the ARL at a specific shift. The details regarding different forms of EQL can be found in Reynolds and Stoumbos, Wu et al, Abujiya et al, and Sanusi et al…”

Section: Performance Evaluation Of Control Charts Based On Nrss Designmentioning

confidence: 99%

“…where δ max and δ min are the maximum and minimum values of the shifts considered in a process, and ARL(δ) denotes the ARL at a specific shift. The details regarding different forms of EQL can be found in Reynolds and Stoumbos, 43 Wu et al, [44][45][46] Abujiya et al, 47 and Sanusi et al 48,49 To study the run length distribution of the control charts based on NRSS, Monte Carlo simulations are carried out using R software 50 for various level of shifts (δ) in the process mean. The ARL and SDRL are computed for assessing the performance to capture a specific level of shift, whereas EQL is used as an overall performance index to detect shifts of various magnitudes.…”

Section: Performance Evaluation Of Control Charts Based On Nrss Designmentioning

confidence: 99%

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A new approach to design efficient univariate control charts to monitor the process mean

Nawaz

Raza

Han

2018

Quality & Reliability Eng

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Designing of efficient control charts for monitoring manufacturing processes and identifying assignable cause of variations is of constant interest in statistical process control. The present study incorporates neoteric ranked set sampling to design more efficient Shewhart, cumulative sum, and exponentially weighted moving average control charts for monitoring the mean of normal process. Using Monte Carlo simulations, the performance of the proposed charts is evaluated and compared with competing control charts on the basis of average run length and standard deviation of the run length for detection of shift of a specific magnitude. The extra quadratic loss is used to assess the overall performance of the control charts to detect shifts of various magnitudes. To further support the findings, a simulation study is carried out which revealed the supremacy of the proposed control charts over the existing charts. A real data set from a combined cycle power plant is used to demonstrate the application of the proposed control charts.

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“…The EQL is defined as

italicEQL = \frac{1}{δ_{\max} - δ_{\min}} \int_{{italicδ}_{italicmin}}^{{italicδ}_{italicmax}} δ^{2} italicARL ((), δ) italicdδ

Section: Performance Evaluation Of Control Charts Based On Nrss Designmentioning

confidence: 99%

Section: Performance Evaluation Of Control Charts Based On Nrss Designmentioning

confidence: 99%

A new approach to design efficient univariate control charts to monitor the process mean

Nawaz

Raza

Han

2018

Quality & Reliability Eng

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“…When auxiliary information is available and the study variable is positively correlated with the auxiliary variable, the ratio estimation method is frequently used to improve the efficiency of the estimators of population characteristics [2]. The ratio-type estimators are now being frequently used in different fields such as process monitoring, designing of acceptance sampling plan for lot sentencing, environmental studies, and forestry are few to mention [3][4][5][6][7][8]. Generally, the information of conventional auxiliary parameters such as the coefficient of kurtosis, the coefficient of skewness, the coefficient of variation, and the coefficient of correlation is used in a linear combination with the sample information of the study and the auxiliary variable to design the estimators of population variance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the efficiency of the ratio-type estimators of population variance with a blend of information on robust location measures

et al. 2019

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Numerous ratio-type estimators of the population variance are proposed in the existing literature based on different characteristics of the study as well as the auxiliary variable. However, mostly the existing estimators are based on the conventional measures of the population characteristics and their efficiency is dubious in the presence of outliers in the data. This study presents improved families of variance estimators under simple random sampling without replacement assuming that the information on some robust nonconventional location parameters of the auxiliary variable is known besides the usual conventional parameters. The bias and mean square error of the proposed families of estimators are obtained and the efficiency conditions are derived mathematically. The theoretical results are supplemented with the numerical illustrations by using real datasets which indicates the supremacy of the suggested families of estimators.

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“…They have shown that the optimal AIB‐DS chart surpasses the optimal AIB synthetic chart. Previous studies have considered several AIB location estimators to construct several AIB‐CUSUM and AIB‐EWMA charts for monitoring the process mean, respectively. A study has increased the sensitivity of the AIB‐EWMA chart using runs rules for monitoring the process mean.…”

Section: Introductionmentioning

confidence: 99%

An enhanced CUSUM‐t chart for process mean

Haq

Abidin

2019

Quality & Reliability Eng

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In this paper, we propose an auxiliary‐information–based (AIB) Crosier cumulative sum (CCUSUM) t chart for monitoring the process mean, namely, the AIB‐CCUSUM‐t chart. The run length characteristics of the proposed chart are computed using Monte Carlo simulation. The optimal parameters for the AIB‐CCUSUM‐t chart to detect specific mean shifts are computed. The fast initial response (FIR) feature is also attached with the proposed chart. It is found that the AIB‐CCUSUM‐t and FIR‐AIB‐CCUSUM‐t charts perform uniformly and substantially better than the CCUSUM‐t and FIR‐CCUSUM‐t charts, respectively. An example is presented to support the theory.

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On efficient CUSUM-type location control charts using auxiliary information

Cited by 43 publications

References 33 publications

A new approach to design efficient univariate control charts to monitor the process mean

A new approach to design efficient univariate control charts to monitor the process mean

Enhancing the efficiency of the ratio-type estimators of population variance with a blend of information on robust location measures

An enhanced CUSUM‐t chart for process mean

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