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Papers by Dr. Moustafa Abushawiesh

International Journal for Quality Research

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial ... more Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out-of-control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent.

Journal of Modern Applied Statistical Methods

The median confidence interval is useful for one parameter families, such as the exponential dist... more The median confidence interval is useful for one parameter families, such as the exponential distribution, and it may not need to be adjusted if censored observations are present. In this article, two estimators for the median of the exponential distribution, MD, are considered and compared based on the sample median and the maximum likelihood method. The first estimator is the sample median, MD 1 , and the second estimator is the maximum likelihood estimator of the median, MD MLE. Both estimators are used to propose a modified confidence interval for the population median of the exponential distribution, MD. Monte Carlo simulations were conducted to evaluate the performance of the proposed confidence intervals with respect to coverage probability, average width and standard error. A numerical example using a real data set is employed to illustrate the use of the modified confidence intervals; results are shown.

Http Dx Doi Org 10 1080 08982110008962599, May 29, 2007

In 1966, Downton introduced a family of estimators based on ordered sample values. Among his fami... more In 1966, Downton introduced a family of estimators based on ordered sample values. Among his family of estimators, Downton proposed an estimator a* for a, the standard deviation of a Normal population.

Quality Engineering, Dec 1, 1999

... Langenberg-Iglewicz (I 0%) - 1.056 -0.065 0.925 1.981 0 In control ... Numerical Examples We ... more ... Langenberg-Iglewicz (I 0%) - 1.056 -0.065 0.925 1.981 0 In control ... Numerical Examples We now illustrate the use of the proposed robust method and compare it with the traditional control chart and the method proposed by Langenberg and Iglewicz (12). ...

Sort Statistics and Operations Research Transactions, 2011

In this paper a robust estimator against outliers along with some other existing interval estimat... more In this paper a robust estimator against outliers along with some other existing interval estimators are considered for estimating the population standard deviation. An extensive simulation study has been conducted to compare and evaluate the performance of the interval estimators. The exact and the proposed robust method are easy to calculate and are not overly computer-intensive. It appears that the proposed robust method is performing better than other confidence intervals for estimating the population standard deviation, specifically in the presence of outliers and/or data are from a skewed distribution. Some real-life examples are considered to illustrate the application of the proposed confidence intervals, which also supported the simulation study to some extent.

Communications in Statistics Simulation and Computation, Aug 31, 2001

This paper has developed a new robust Shewhart-type control chart for monitoring the location of ... more This paper has developed a new robust Shewhart-type control chart for monitoring the location of a bivariate process and examine its behavior based on the Hodges–Lehamnn and Shamos–Bickel–Lehmann estimators. A numerical example is given to illustrate the use of the proposed method. Its performance is investigated using a simulation study.

International Journal of Statistics in Medical Research, 2014

This paper investigates the performance of ten methods for constructing a confidence interval est... more This paper investigates the performance of ten methods for constructing a confidence interval estimator for the population standard deviation by a simulation study. Since a theoretical comparison among the interval estimators is not possible, a simulation study has been conducted to compare the performance of the selected interval estimators. Data were randomly generated from several distributions with a range of sample sizes. Various evaluation criterions are considered for performance comparison. Two health related data have been analyzed to illustrate the application of the proposed confidence intervals. Based on simulation results, some intervals with the best performance have been recommended for practitioners.

Quality Engineering, 2000

International Journal of Quality & Reliability Management, 2009

Purpose – This paper seeks to propose a univariate robust control chart for location and the nece... more Purpose – This paper seeks to propose a univariate robust control chart for location and the necessary table of factors for computing the control limits and the central line as an alternative to the Shewhart X¯ control chart. Design/methodology/approach – The proposed method is based on two robust estimators, namely, the sample median, MD, to estimate the process mean, μ, and the median absolute deviation from the sample median, MAD, to estimate the process standard deviation, σ. A numerical example was given and a simulation study was conducted in order to illustrate the performance of the proposed method and compare it with that of the traditional Shewhart X¯ control chart. Findings – The proposed robust X¯MDMAD control chart gives better performance than the traditional Shewhart X¯ control chart if the underlying distribution of chance causes is non-normal. It has good properties for heavy-tailed distribution functions and moderate sample sizes and it compares favorably with the traditional Shewhart X¯ control chart. Originality/value – The most common statistical process control (SPC) tool is the traditional Shewhart X¯ control chart. The chart is used to monitor the process mean based on the assumption that the underlying distribution of the quality characteristic is normal and there is no major contamination due to outliers. The sample mean, X¯, and the sample standard deviation, S, are the most efficient location and scale estimators for the normal distribution often used to construct the X¯ control chart, but the sample mean, X¯, and the sample standard deviation, S, might not be the best choices when one or both assumptions are not met. Therefore, the need for alternatives to the X¯ control chart comes into play. The literature shows that the sample median, MD, and the median absolute deviation from the sample median, MAD, are indeed more resistant to departures from normality and the presence of outliers.

We consider some confidence intervals for estimating the population median. Some robust estimator... more We consider some confidence intervals for estimating the population median. Some robust estimators for the standard error of the sample median against outliers are considered to construct confidence intervals that are more resistant to outliers than the Student t confidence interval. The confidence intervals based on these standard errors are computed and compared with each other under normal and skewed distributions. A Monte Carlo simulation study has been undertaken to compare the performance of the proposed confidence intervals by calculating the coverage probability and the average width. The simulation study shows that our proposed confidence intervals give good coverage probability for skewed distributions and have shorter lengths. Some real-life examples have been considered that support the findings of the paper to some extent.