Abstract:Nonparametric (distribution-free) control charts have been introduced in recent years when quality characteristics do not follow a specific distribution. When the sample selection is prohibitively expensive, we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts. In this study, we proposed a nonparametric homogeneously weighted moving average based on the Wilcoxon signed-rank test with ranked set sampling… Show more
“…The primary goal of this research is to determine IC and OC RL using L sigma limits under the assumption of the Monte Carlo simulation method. Many researchers Abid et al., 22 Noor‐ul‐Amin et al., 23 Aslam and Anwar, 24 Abbasi et al., 12 Rasheed et al., 25 Zhang et al., 26 Almanjahie et al., 27 and Arslan et al 19 . used L sigma limits under Monte Carlo simulation for the performance evaluation of EWMA charts in various setup.…”
Memory control charts or charts, such as cumulative sums and exponentially weighted moving average (EWMA), are widely used for tracking minor to moderate changes in process parameters. The literature presented an EWMA chart for monitoring exponential processes, assuming probability limits to determine both in‐ and out‐of‐control average run length (ARL). The use of probability limits for memory charts is an incorrect method. So, we are aiming to correct the EWMA chart by Aslam et al. and the proposed EWMA chart (represented by IEWMAE) for the exponential process using the run‐length (RL) method based on the simulation method instead of the probability‐based method. The ARL, standard deviation of RL, and median of RL are used to evaluate the proposed chart's performance. For practical consideration of the proposed chart, a real‐world example of a urinary tract infection is also presented.
“…The primary goal of this research is to determine IC and OC RL using L sigma limits under the assumption of the Monte Carlo simulation method. Many researchers Abid et al., 22 Noor‐ul‐Amin et al., 23 Aslam and Anwar, 24 Abbasi et al., 12 Rasheed et al., 25 Zhang et al., 26 Almanjahie et al., 27 and Arslan et al 19 . used L sigma limits under Monte Carlo simulation for the performance evaluation of EWMA charts in various setup.…”
Memory control charts or charts, such as cumulative sums and exponentially weighted moving average (EWMA), are widely used for tracking minor to moderate changes in process parameters. The literature presented an EWMA chart for monitoring exponential processes, assuming probability limits to determine both in‐ and out‐of‐control average run length (ARL). The use of probability limits for memory charts is an incorrect method. So, we are aiming to correct the EWMA chart by Aslam et al. and the proposed EWMA chart (represented by IEWMAE) for the exponential process using the run‐length (RL) method based on the simulation method instead of the probability‐based method. The ARL, standard deviation of RL, and median of RL are used to evaluate the proposed chart's performance. For practical consideration of the proposed chart, a real‐world example of a urinary tract infection is also presented.
“…In the third paper entitled "Ranked-Set Sampling Based Distribution Free Control Chart with Application in CSTR Process" by Almanjahie et al [3], the authors suggested a nonparametric homogeneously weighted moving average for detecting shifts in the process location of a continuous and symmetric distribution based on the Wilcoxon signed-rank test with ranked set sampling control chart.…”
The interface between computer science and statistics has developed considerably in recent years, with exponential progress in the fields of data analysis, stochastic modeling, machine learning, econometrics, simulation, algorithms, classification, and networks. Innovative discoveries in this field appear every day, opening new scientific horizons for the modern world. This is especially true in the post-2020 period, with the treatment of large volumes of data that feed the daily operations of large corporations, as well as the development of artificial intelligence, including advanced machine learning techniques, particularly "deep learning".The aim of the special issue titled "New Trends in Statistical Computing and Data Science" was to publish the most significant articles in these directions, that is, the current progress of statistical computing and data science. Novelty, high quality, and importance are the triptych of the special issue.
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