On the Effect of Estimation Error for the Risk-Adjusted Charts
Control charts are a popular statistical process control (SPC) technique for monitoring to detect the unusual variations in different processes. Contrary to the classical charts, control charts have also been modified to include covariates using regression approaches. This study assesses the perform...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6258010 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547069607280640 |
|---|---|
| author | Sajid Ali Naila Altaf Ismail Shah Lichen Wang Syed Muhammad Muslim Raza |
| author_facet | Sajid Ali Naila Altaf Ismail Shah Lichen Wang Syed Muhammad Muslim Raza |
| author_sort | Sajid Ali |
| collection | DOAJ |
| description | Control charts are a popular statistical process control (SPC) technique for monitoring to detect the unusual variations in different processes. Contrary to the classical charts, control charts have also been modified to include covariates using regression approaches. This study assesses the performance of risk-adjusted control charts under the complexity of estimation error by considering logistic and negative binomial regression models. To be more precise, risk-adjusted Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) charts are used to evaluate the impact of the estimation error. To compute the average run length (ARL), Markov Chain Monte Carlo simulations are conducted. Furthermore, a bootstrap method is also used to compute the ARL assuming different Phase-I data sets to minimize the effect of estimation error on risk-adjusted control charts. The results for cardiac surgery and respiratory disease data sets show that the modified control charts improve the performance in detecting small shifts. |
| format | Article |
| id | doaj-art-315c77ff72fb4aaf8fed5d4a33457737 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-315c77ff72fb4aaf8fed5d4a334577372025-02-03T06:46:00ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/62580106258010On the Effect of Estimation Error for the Risk-Adjusted ChartsSajid Ali0Naila Altaf1Ismail Shah2Lichen Wang3Syed Muhammad Muslim Raza4Department of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanCollege of Life Science, Linyi University, Linyi, Shandong 276000, ChinaDepartment of Quantitative Methods, School of Business and Economics, University of Management and Technology, Lahore, PakistanControl charts are a popular statistical process control (SPC) technique for monitoring to detect the unusual variations in different processes. Contrary to the classical charts, control charts have also been modified to include covariates using regression approaches. This study assesses the performance of risk-adjusted control charts under the complexity of estimation error by considering logistic and negative binomial regression models. To be more precise, risk-adjusted Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) charts are used to evaluate the impact of the estimation error. To compute the average run length (ARL), Markov Chain Monte Carlo simulations are conducted. Furthermore, a bootstrap method is also used to compute the ARL assuming different Phase-I data sets to minimize the effect of estimation error on risk-adjusted control charts. The results for cardiac surgery and respiratory disease data sets show that the modified control charts improve the performance in detecting small shifts.http://dx.doi.org/10.1155/2020/6258010 |
| spellingShingle | Sajid Ali Naila Altaf Ismail Shah Lichen Wang Syed Muhammad Muslim Raza On the Effect of Estimation Error for the Risk-Adjusted Charts Complexity |
| title | On the Effect of Estimation Error for the Risk-Adjusted Charts |
| title_full | On the Effect of Estimation Error for the Risk-Adjusted Charts |
| title_fullStr | On the Effect of Estimation Error for the Risk-Adjusted Charts |
| title_full_unstemmed | On the Effect of Estimation Error for the Risk-Adjusted Charts |
| title_short | On the Effect of Estimation Error for the Risk-Adjusted Charts |
| title_sort | on the effect of estimation error for the risk adjusted charts |
| url | http://dx.doi.org/10.1155/2020/6258010 |
| work_keys_str_mv | AT sajidali ontheeffectofestimationerrorfortheriskadjustedcharts AT nailaaltaf ontheeffectofestimationerrorfortheriskadjustedcharts AT ismailshah ontheeffectofestimationerrorfortheriskadjustedcharts AT lichenwang ontheeffectofestimationerrorfortheriskadjustedcharts AT syedmuhammadmuslimraza ontheeffectofestimationerrorfortheriskadjustedcharts |