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Distribution-free or nonparametric control charts are used for monitoring the process parameters when there is a lack of knowledge about the underlying distribution. In this paper, we investigate a single distribution-free triple exponentially weighted moving average control chart based on the Lepage statistic (referred as TL chart) for simultaneously monitoring shifts in the unknown location and scale parameters of a univariate continuous distribution. The design and implementation of the proposed chart are discussed using time-varying and steady-state control limits for the zero-state case. The run-length distribution of the TL chart is evaluated by performing Monte Carlo simulations. The performance of the proposed chart is compared to those of the existing EWMA-Lepage (EL) and DEWMA-Lepage (DL) charts. It is observed that the TL chart with a time-varying control limit is superior to its competitors, especially for small to moderate shifts in the process parameters. We also provide a real example from a manufacturing process to illustrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]

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 this article, we develop a new Triple EWMA-Max (TEWMA-Max) control chart for joint monitoring of the process mean and variance. The run-length characteristics of the proposed chart are computed through Monte-Carlo simulations. A detailed comparative study is conducted to compare the performance of the proposed chart with that of the EWMA-Max and DEWMA-Max charts. The comparison results reveal that the TEWMA-Max chart is more sensitive than the EWMA-Max chart in detecting small to moderate shifts in the mean and variability. Furthermore, it is more efficient than the DEWMA-Max chart for small to moderate shifts in the mean and small to large downward and large upward shifts in the dispersion, while it is quite comparable for small upward shifts in the variability with an increase of the smoothing parameter λ. Additionally, the TEWMA-Max chart shows a satisfactory overall performance for detecting a wide range of shift combinations in the process mean and dispersion. Finally, real and simulated datasets are considered to present the implementation of the new 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]

In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the S 2 -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the S 2 -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the S 2 -DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new S 2 -DGWMA chart. [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]

The moving average (MA) control chart is a memory-type scheme and effective for monitoring small and moderate shifts in the process mean. Several modifications of the MA chart, such as the double MA (DMA) and the triple MA (TMA) charts, have been introduced to improve the detection ability of the MA chart. In the present article, we propose the quadruple MA (QMA) chart for normally distributed data to improve much more the performance of the MA chart for small shifts. Comparing with the existing MA, DMA, TMA, EWMA and DEWMA charts, it is shown that the proposed chart is more effective for small shifts. Furthermore, it is found that the QMA chart is more in-control (IC) robust to non normality than its competitors. Finally, an illustrative example is provided to demonstrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]

The repetitive sampling scheme is utilized in control charts to enhance their detection ability in process shifts. Their performance is usually evaluated under the assumption that the process parameters are known. In this article, we investigate the performance of the X ¯ and S2 charts under repetitive sampling when the process parameters are estimated from a preliminary in-control (Phase I) sample. We also present the exact expressions of the average run-length (ARL), standard deviation of run-length (SDRL) and average sample number (ASN) measures. A numerical analysis demonstrates that the effect of parameters estimation is significant when the Phase I sample and the sample size are small. [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]

Control charts are widely known 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 double generally weighted moving average chart (referred as Max-DGWMA), that simultaneously detects shifts in the process mean and/or process dispersion. The run length performance of the proposed Max-DGWMA chart is compared with that of the Max-EWMA, Max-DEWMA, Max-GWMA and SS-DGWMA charts, using time-varying control limits, through Monte–Carlo simulations. The comparisons reveal that the proposed chart is more efficient than the Max-EWMA, Max-DEWMA and Max-GWMA charts, while it is comparable with the SS-DGWMA chart. An automotive industry application is presented in order to implement the Max-DGWMA chart. The goal is to establish statistical control of the manufacturing process of the automobile engine piston rings. The source of the out-of-control signals is interpreted and the efficiency of the proposed chart in detecting shifts faster is evident. [ABSTRACT FROM AUTHOR]

Foodborne listeriosis is one of the most serious and severe foodborne diseases, with a high mortality rate worldwide. Listeria monocytogenes' (Lm) ability to survive under a diversity of conditions makes it a threat for food safety. Soft and semisoft cheeses are common RTE foods that support survival and growth of Lm due to their high moisture content and favorable pH. The aim of the present study was to assess, after artificial inoculation, the growth potential (Δ) of Lm in vacuum packed RTE soft Greek Anthotyros cheese. Growth potential of Lm was determined according to the new version of "EURL Lm Technical Guidance Document on challenge tests and durability studies for assessing shelf life of ready-to-eat foods related to Lm". The results of our study revealed that the growth potential of Lm was (Δ) = 4.93 log10 CFU/g, indicating that the specific soft cheese "anthotyros" is a "Ready to eat food able to support growth of Lm and classified in food safety category 1.2 in the (EC) Regulation 2073/2005". That means that the food business operator (FBO) must comply with the criteria that define that the bacterium must not be detected in 25 g (n = 5, c = 0) at the time of leaving the production plant. Through this study, we try to highlight the need for FBO to conduct relevant research to ensure that the ready-to-eat products which they produce, during their shelf life, do not support the growth of Lm. [ABSTRACT FROM AUTHOR]

The modified exponentially weighted moving average (MEWMA) control chart has been proposed in the literature in an effort to improve the performance of the classical EWMA chart. In this article, we study in more detail the MEWMA chart using various values of its additional design parameter and we also propose the double MEWMA (DMEWMA) chart for monitoring shifts in the process mean, under the assumption that the underlying process follows a normal distribution. The statistical performance of the two charts has been evaluated on the basis of the average and standard deviation of run-length performing Monte Carlo simulations. A comparison study versus the EWMA and DEWMA charts indicate that the modified schemes have better detection ability for the range of shifts where a classical chart is not optimal designed. Finally, one real example is given to demonstrate the application of the proposed chart. [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]

In this article, we extend the single exponentially weighted moving average (EWMA) semicircle (EWMA‐SC) chart to a single double EWMA semicircle chart, called the DEWMA‐SC control chart. The run length (RL) characteristics of the proposed chart are computed through Monte‐Carlo simulations. A detailed comparative study is conducted to compare the performance of the proposed chart with that of the EWMA‐SC and GWMA‐SC charts with time‐varying control limits. The comparison results reveal that the DEWMA‐SC chart outperforms the competing charts in detecting most of the considered shifts in the process mean and/or variability. Additionally, the DEWMA‐SC chart shows very satisfactory overall performance for detecting a wide range of shift combinations of the process mean and dispersion, compared with the EWMA‐SC and GWMA‐SC charts assuming various control chart parameter values. Finally, real and simulated datasets are considered to present the implementation of the new chart. [ABSTRACT FROM AUTHOR]

Statistical process monitoring (SPM) is mostly populated with univariate control charts used to monitor a single variable (or quality characteristic). Nowadays, industries and online environments are filled with processes in which two or more quality characteristics are related. In such situations, univariate control charts are replaced with multivariate control charts for the sake of monitoring several characteristics simultaneously. This paper develops a new multivariate triple exponentially weighted moving average (MTEWMA) chart to serve this purpose. Moreover, the design of the multivariate simple and double exponentially weighted moving average (denoted as MEWMA and MDEWMA) charts are revisited using extensive simulations. It is observed that the MTEWMA chart has very interesting time‐varying properties as compared to the asymptotic properties. The newly proposed MTEWMA chart is superior over the MEWMA and MDEWMA charts in many situations of the asymptotic control limits. An illustrative example is provided to demonstrate the sensitivity of the proposed charts. [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]

In row designs, n experimental units are arranged in time or along a line. Every experimental unit is allocated to one out of ν treatments. The purpose of this paper is to determine optimal row designs for estimating means, that is treatment effects, under the model of main effects with homogeneous population. The errors of the observations follow a first-order autoregressive process with parameter ρ. For ν = 3 , we show that the design d (3 m) : 1 3 2 ⋯ 1 3 2 ⏟ 3 m is D-optimal, when n = 3m, for any 0 < ρ < 1 and any m ≥ 2. Also, we find and construct the A-optimal designs, when n = 3m, for any 0 < ρ < 1 and any m ≥ 2. Moreover, we prove that the designs d (3 m + 1) : 1 3 2 ⋯ 1 3 2 ⏟ 3 m 1 and d (3 m + 2) : 1 2 3 ⋯ 1 2 3 ⏟ 3 m 12 are D-optimal, when n = 3m + 1 and n = 3m + 2, respectively, for any 0 < ρ < 1 and any m ≥ 1. Furthermore, we present the competing designs for some values of n, when ν ≥ 4 and − 1 < ρ < 1. [ABSTRACT FROM AUTHOR]

In many real applications, the quality characteristics of interest, such as the number of nonconformities in a production unit or the annual incidence rate, may follow the Poisson distribution. In the present article, we propose a triple exponentially weighted moving average control chart for monitoring Poisson processes (regarded as PTEWMA chart). One‐ and two‐sided schemes of the proposed chart are designed and are compared with the existing PEWMA and PDEWMA charts in terms of the average run‐length (ARL). The results show that for small values of the smoothing parameter λ, the PTEWMA chart is more effective than its competitors in detecting small shifts while its superiority increases as the value of λ increases. Moreover, the two‐sided PTEWMA chart is less ARL‐biased for small downward shifts than the other two charts. Two illustrative examples are provided to demonstrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]

The exponentially weighted moving average (EWMA) control chart is a very popular memory-type chart and also effective in detecting small shifts in the process mean. Several modifications of the EWMA chart, such as the double and triple EWMA charts (regarded as DEWMA and TEWMA charts, respectively) have been developed to enhance its performance in detecting small shifts. In the present article, we propose the quadruple EWMA chart (regarded as QEWMA chart) in order to improve much more the detection ability of the EWMA chart. The run-length characteristics of the proposed chart are evaluated by performing Monte Carlo simulations. Comparing with the EWMA, DEWMA and TEWMA charts, it is found that the QEWMA chart outperforms its competitors for small shifts. Moreover, it is shown that the proposed chart is more in-control (IC) robust under several non-normal distributions than the other charts, especially for a medium value of the smoothing parameter. The effect of inertia on the performance of the QEWMA chart is also investigated as a part of this article. Finally, two examples are provided to demonstrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted moving average (DHWMA) control chart based on the sign statistic is proposed for monitoring the location parameter of an unknown and continuous distribution. The performance of the proposed chart is measured through the run-length distribution and its associated characteristics by performing Monte Carlo simulations. The DHWMA sign chart is compared with other nonparametric sign charts, such as the homogeneously weighted moving average, generally weighted moving average (GWMA), double GWMA, and triple exponentially weighted moving average sign charts, as well as the traditional DHWMA chart. The results indicate that the proposed chart performs just as well as and in some cases better than its competitors, especially for small shifts. Finally, two examples are provided to show the application and implementation of the proposed chart. [ABSTRACT FROM AUTHOR]

Control charts are widely known 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 sum of squares triple exponentially weighted moving average control chart (referred as SS‐TEWMA chart), that simultaneously detects shifts in the process mean and/or process dispersion. The run length performance of the proposed SS‐TEWMA control chart is compared with that of the sum of squares EWMA, sum of squares double EWMA, sum of squares generally weighted moving average, and sum of squares double generally weighted moving average, control charts, through Monte Carlo simulations. The comparisons indicate that the proposed chart is more efficient, than the competing ones, in detecting small shifts in the process mean and/or variability for most of the considered scenarios, while it has comparable performance for some others in identifying large shifts in the process mean and small to large shifts in the process variability. Finally, two illustrative examples are provided to explain the application of the SS‐TEWMA control chart. [ABSTRACT FROM AUTHOR]

Nonparametric (or distribution‐free) control charts are used for monitoring processes where there is a lack of knowledge about the underlying distribution. In this article, a triple exponentially weighted moving average control chart based on the signed‐rank statistic (referred as TEWMA‐SR chart) is proposed for monitoring shifts in the location parameter of an unknown, but continuous and symmetric, distribution. The run‐length characteristics of the proposed chart are evaluated performing Monte Carlo simulations. A comparison study with other existing nonparametric control charts based on the signed‐rank statistic, the TEWMA sign chart, and the parametric TEWMA‐X¯ chart indicates that the proposed chart is more effective in detecting small shifts, while it is comparable with the other charts for moderate and large shifts. Finally, two illustrative examples are provided to demonstrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]