Robust Estimation in Multivariate Control Charts for Individual Observations
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Summary
Simulation studies show that the T2 control chart using the minimum volume ellipsoid (MVE) estimators is effective in detecting any reasonable number of outliers.
- Type
- article
- Published
- 2003-10-01
- Cited by
- 197
- References
- 20
- OpenAlex
- https://openalex.org/W47198768
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:115456218
Keywords
Control chart, Outlier, Estimator, Multivariate statistics, Statistics
References
- A Comparison of Multivariate Control Charts for Individual Observations
- Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation
- Robust M-Estimators of Multivariate Location and Scatter
- A Fast Algorithm for the Minimum Covariance Determinant Estimator
- THE MULTIVARIATE SHORT-RUN SNAPSHOT Q CHART
- Robust control charts
- Adapting control charts for the preliminary analysis of multivariate observations
- Robust Estimation of Dispersion Matrices and Principal Components
- The stalactite plot for the detection of multivariate outliers
- Heuristic Search Algorithms for the Minimum Volume Ellipsoid
- Unmasking Multivariate Outliers and Leverage Points
- Multivariate Control Charts for Individual Observations
- Least Median of Squares Regression
- Multivariate statistical process control—recent results and directions for future research
- Multivariate Quality Control
- Unmasking Multivariate Outliers and Leverage Points: Comment
- Xq and Rq charts: Robust control charts
- Robust Regression and Outlier Detection
- Robust Control Charts
- Robust Distances: Simulations and Cutoff Values
Cited by
- Monitoring Correlation Within Linear Profiles Using Mixed Models
- Profile Monitoring via Nonlinear Mixed Models
- Robust Monitoring of Contaminated Data
- Cluster-Based Profile Analysis in Phase I
- A Distribution-Free Control Chart for Retrospective Location Analysis of Subgrouped Multivariate Data
- Optimal Monitoring of Multivariate Data for Fault Patterns
- An Overview of Phase I Analysis for Process Improvement and Monitoring
- A Nonrigorous Approach Of Incorporating Sensitizing Rules Into Multivariate Control Charts
- Multivariate control chart based on robust estimator
- Nonlinear Profile Data Analysis for System Performance Improvement
- Statistical Process Control Using Modified Robust Hotelling's T² Control Charts
- Real‐time covariance estimation for the local level model
- Nonlinear Profile Monitoring of Reflow Process Data Based on the Sum of Sine Functions
- Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications
- A Distribution‐Free Multivariate Control Chart for Phase I Applications
- Analysis Of The Stability Of Inflation In Inflation Targeting Countries Via Control Charts
- Alternative Hotelling's T2 Charts using Winsorized Modified One‐Step M‐estimator
- A Multistep, Cluster-Based Multivariate Chart for Retrospective Monitoring of Individuals
- A Semiparametric Mixed Model Approach to Phase I Profile Monitoring
- Using Control Charts to Monitor Process and Product Quality Profiles
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