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Control charts are very popular quality tools used to detect and control industrial process deviations in Statistical Process Control. In the current paper, we propose a new single memory-type control chart, called the maximum triple exponentially weighted moving average chart (referred as Max-TEWMA chart), that simultaneously detects both upward and downward shifts in the process mean and/or process dispersion. The run length performance and the diagnostic ability of the Max-TEWMA control chart are compared with that of the Max-EWMA, Max-DEWMA and Max-GWMA charts, through Monte-Carlo simulations. The comparisons reveal that the proposed chart is more efficient, than the competing ones, in detecting shifts in the process mean and variability simultaneously. Furthermore, the Max-TEWMA chart provides a satisfactory overall performance for identifying a wide range of shifts in the process mean and variability. Finally, two illustrative examples are presented to explain the application of the Max-TEWMA control chart. [ABSTRACT FROM AUTHOR]

In many real world problems it is of interest to ascertain which factors are most relevant for determining a given outcome. This is the so-called variable selection problem. The present paper proposes a new regression model for its solution. We show that the proposed model satisfies continuity, sparsity, and unbiasedness properties. A generalized Krylov subspace method for the practical solution of the minimization problem involved is described. This method can be used for the solution of both small-scale and large-scale problems. Several computed examples illustrate the good performance of the proposed model. We place special focus on screening studies using saturated and supersaturated experimental designs. [ABSTRACT FROM AUTHOR]

Control charts based on the sample mean X ¯ are used for monitoring shifts in the process mean under the assumption that the process standard deviation is stable or well estimated. However, in many applications, this assumption is violated. In this case, t control charts are preferred as they are robust to changes in the process standard deviation. In this article, a generally weighted moving average chart based on the t statistic (referred as GWMA t chart) is proposed for monitoring shifts in the process mean. Its performance is studied using Monte Carlo simulation. It is shown that the proposed chart is more effective than the Shewhart t and EWMA t charts. Moreover, it is found more robust than the GWMA X ¯ chart to changes in the process standard deviation. Finally, two illustrative examples are given to display the application of the proposed chart. [ABSTRACT FROM AUTHOR]

Abstract Single control charts are widely used to control assignable causes that shift the process due to variations in the mean and the dispersion. In the present article, the exponentially weighted moving average maximum (EWMA-Max) control chart is extended to the new single generally weighted moving average maximum (referred as GWMA-Max) control chart for joint monitoring of the process mean and variability. The proposed chart is compared with the EWMA-Max and DEWMA-Max charts in terms of the run-length performance measures. The results reveal that the GWMA-Max chart is very efficient in detecting small shifts in the process mean and variability concurrently. Finally, a practical implementation of the GWMA-Max chart is displayed using real and simulated datasets. [ABSTRACT FROM AUTHOR]

A double moving average (DMA) control chart has been proposed in the literature for monitoring the process mean. Several studies have also been done based on this scheme. Unfortunately, the variance of DMA statistic that has been used in these studies is not correct. In this article, we provide the correct variance of the DMA chart and through a simulation study, we evaluate its performance. It is shown that the DMA chart is more effective than the moving average (MA) chart in detecting small shifts in the process mean and vice versa for moderate to large shifts. Moreover, the DMA chart, even with a small value of span w, has similar, and in some cases better, detection ability than the EWMA and CUSUM charts, especially for moderate and large shifts. [ABSTRACT FROM AUTHOR]

In many industrial and non-industrial processes, the quality of a product or a process are described by a functional relationship between a response variable and one or more independent variables, known as profile. On the other hand, the process capability indices provide numerical measures of the process ability. Few researches have been done to evaluate the process capability of profiles; especially for linear profiles. In the present article, we calculate the S pmk index to measure the process capability for gamma regression profiles and we also construct an approximate confidence interval using the bootstrap method. The performance of the proposed method is assessed through a simulation study. Moreover, we show that the S pmk index is very robust when the true distribution of data is a lognormal or a Weibull. Finally, the applicability of the proposed index is illustrated using two real examples. [ABSTRACT FROM AUTHOR]

The solution of the penalized least squares problems depends on a tuning parameter. A popular tool for specifying the tuning parameter is the generalized cross-validation (GCV). In this work, we utilize estimates for the GCV function whose minimizers can lead to the determination of the tuning parameter. The selection of an efficient estimate depends on an appropriately defined index of proximity. Bounds and specific values are derived for this index, and a thorough study proves that the proposed one-term estimate suits perfectly to statistical models with high correlated variables. This is confirmed through simulation tests for several datasets. [ABSTRACT FROM AUTHOR]

The Conway-Maxwell Poisson (COM-Poisson) distribution is a generalization of the Poisson distribution and encompasses the geometric, the Poisson and the Bernoulli distribution as special cases. This distribution can be used to model over- or under- dispersed data in manufacturing processes. For monitoring such data, a flexible memory-type control chart based on the progressive mean (PM) statistic (regarded as CMP-PM chart) is developed in the present paper. Through a simulation study, we investigate the run-length distribution of the proposed chart. The performance comparison study shows that the CMP-PM chart outperforms the Sellers, the GEWMA and the μ-CUSUM charts at almost all levels of shifts for both over- and under-dispersed data. Finally, the application of the proposed chart is given through an illustrative example. [ABSTRACT FROM AUTHOR]

tIn high quality processes when the events occur rarely, the times between events (TBE) are observed and also monitored instead of the number of events. In this case, it can be assumed that the times between events follow a gamma distribution. A one-sided Double Exponentially Moving Average control chart to monitor the TBE (regarded as DEWMA-TBE chart) is proposed in the present work. The design parameters of the proposed chart are provided and through a simulation study, it is seen that the DEWMA-TBE chart is more effective than the EWMA and Shewhart charts at detecting downward shifts. [ABSTRACT FROM AUTHOR]

AbstractSeveral versions of the exponentially weighted moving average (EWMA) control chart, such as the generally weighted moving average (GWMA), the double, triple, and quadruple EWMA (DEWMA, TEWMA, and QEWMA), and the homogeneously weighted moving average (HWMA) charts, have been developed to enhance its detection ability, especially for small and moderate shifts. These charts have been criticized as it is considered that an appropriately designed EWMA chart provides as good or better zero-state and steady-state average run-length (ARL) performance. In this article, we perform a careful comparison study of the above control charts under similar in-control (IC) run-length properties. We show that the “new” memory-type control charts have better out-of-control (OOC) run-length characteristics, especially for small to moderate shifts in the process mean for both the zero-state and steady-state cases, except for the HWMA chart where its steady-state OOC performance is very poor. We also present a comparison study based on the worst-case ARL measure. Finally, the extensions of the EWMA chart have better IC robustness ability than the EWMA and HWMA charts. [ABSTRACT FROM AUTHOR]

Letbe a symmetric matrix. Stochastic estimates have been introduced in the literature for estimating the trace of a function of the matrixAby computing the mean value of the quantitiesxTif(A)xi, wherexiare appropriate sample vectors forming the columns of a sample matrixX. In this work, the selection of the vectorsxifrom the columns of a Hadamard matrix is discussed and the employment of the Hadamard matrices for the construction of the sample matrixXis studied and tested throughout several experiments. In addition, the combination of Hadamard matrices with a central-composite design (CCD) for the construction of the sample matrixXis introduced. [ABSTRACT FROM AUTHOR]