ArticleScientific reports2022
Monitoring non-parametric profiles using adaptive EWMA control chart.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An enhanced nonparametric quality control chart with application related to industrial process.Scientific reports · 2024Article
- Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles.Scientific reports · 2024Article
- Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism.Scientific reports · 2023Article
- Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.Frontiers in public health · 2023Article
- A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring.PloS one · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts.
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