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Yu-Jau Lin Hassan M. Okasha Abdulkareem M. Basheer Yuh Long Lio 《Quality and Reliability Engineering International》2023,39(3):931-957
The estimations of parameters and percentiles for a three-parameter Marshall Olkin extended inverse Weibull distribution based on progressively type-II censored sample are concerned. The Bayesian, least-squares, maximum likelihood and percentiles estimate methods for the model parameters have been developed. Comparing all estimate methods, the least-squares, maximum likelihood and percentiles estimate methods are shown not stable due to the identification problem in the extended parametric space. Therefore, the Bayes estimate methods are focused. Three Bayesian estimations of the distribution parameters and p percentiles for 2.5, 50 and 97.5 under the squared error loss, absolute error loss and LINEX loss functions are respectively calculated by using the Markov chain Monte Carlo sampling procedure. Moreover, two real data sets are presented for the application illustration. Finally, concluding remarks are addressed. 相似文献
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Waldyn G. Martinez Maria L. Weese L. Allison Jones-Farmer 《Quality and Reliability Engineering International》2020,36(4):1272-1295
In phase I of statistical process control (SPC), control charts are often used as outlier detection methods to assess process stability. Many of these methods require estimation of the covariance matrix, are computationally infeasible, or have not been studied when the dimension of the data, , is large. We propose the one-class peeling (OCP) method, a flexible framework that combines statistical and machine learning methods to detect multiple outliers in multivariate data. The OCP method can be applied to phase I of SPC, does not require covariance estimation, and is well suited to high-dimensional data sets with a high percentage of outliers. Our empirical evaluation suggests that the OCP method performs well in high dimensions and is computationally more efficient and robust than existing methodologies. We motivate and illustrate the use of the OCP method in a phase I SPC application on a , dimensional data set containing Wikipedia search results for National Football League (NFL) players, teams, coaches, and managers. The example data set and R functions, OCP.R and OCPLimit.R, to compute the respective OCP distances and thresholds are available in the supplementary materials. 相似文献
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Amjad Javaid Muhammad Noor-ul-Amin Muhammad Hanif 《Quality and Reliability Engineering International》2021,37(8):3262-3281
Hybrid control charts have become part of statistical process control (SPC) but still, need more emphasis. Researchers are developing charts for joint monitoring of process mean and variance shifts just like Max-EWMA and their hybrid version using auxiliary information but are ignoring the effect of measurement error on the efficiency of charts. We propose maximum hybrid exponentially weighted moving average with measurement error using auxiliary information and name it Max-HEWMAMEAI control chart. The efficiency of this chart is proved through calculations of average run lengths ( ) and standard deviations of run lengths (SDRLs) using the Monte Carlo simulations method whereas, and are shown in tabular form. The effect of measurement error on the efficiency of the chart has been analyzed and the impact of multiple measurements to reduce the error effect has been studied using the covariate model. Real-life application is also part of this article to support the simulation results. 相似文献
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XueLong Hu Philippe Castagliola AnAn Tang XiaoJian Zhou PanPan Zhou 《Quality and Reliability Engineering International》2020,36(3):1111-1131
Synthetic-type charts are efficient tools for process monitoring. They are easy to design and implement in practice. The properties of these charts are usually evaluated under the assumption of known process parameters. This assumption is sometimes violated in practice, and process parameters have to be estimated from different phase I data sets collected by different practitioners. This fact causes the between-practitioners variability among the properties of the synthetic-type charts designed for each practitioner. In fact, the shape of the run length distribution of the synthetic-type charts changes with the mean shift size. As a good alternative, the median run length metric is argued to evaluate the properties of different control charts. In this paper, the is used as a measure of the synthetic chart's performance, and the conditional properties of the synthetic chart with unknown process parameters are investigated. Both the average ( ) and the standard deviation of ( ) are used together to investigate the chart's properties when the process parameters are unknown. If the available number of phase I samples is not large enough to reduce the variability of the in-control values to an acceptable level, a bootstrap-type approach is suggested to adjust the control limits of the synthetic chart and to further prevent many unwanted lower in-control values. 相似文献
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Chelsea L. Jones Abdel-Salam G. Abdel-Salam D'Arcy Mays 《Quality and Reliability Engineering International》2021,37(3):857-881
Profile monitoring is one of the methods used in statistical process control ( ) to understand the functional relationship between response and explanatory variables by tracking this relationship and estimating parameters. SPC is done in two phases: In Phase , a statistical model is created and its parameters estimated using historical data. Phase implements the statistical model and monitors the live ongoing process. Control charts are graphical tools used to monitor these functional relationships over time in both Phase and Phase . This study provides a step-by-step application for parametric, nonparametric, and semiparametric methods in profile monitoring and creates an in-depth guideline with comparative analysis studies for novice practitioners. A comparative analysis under each distributional assumption is conducted for various control charts. 相似文献