Introduction of robust multivariate process capability indices
Journal of Statistical Computation and Simulation, cilt.96, sa.5, ss.1176-1201, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 96 Sayı: 5
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/00949655.2025.2582772
- Dergi Adı: Journal of Statistical Computation and Simulation
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MathSciNet, zbMATH
- Sayfa Sayıları: ss.1176-1201
- Anahtar Kelimeler: Multivariate process capability indices, multivariate quality control, outlier, robust M-estimator, statistical quality control
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
Process Capability Indices (PCIs) are essential for evaluating process performance, but their accuracy depends on correctly estimating distribution parameters. Classical methods, such as Maximum Likelihood Estimation (MLE), are highly sensitive to outliers, often leading to misleading conclusions about process capability. To address this issue, this study investigates robust estimation techniques enhancing the reliability of parameter estimation in the presence of contaminated data. Both univariate and Multivariate Process Capability Indices (MPCIs) are reviewed, and robust estimators for multivariate process parameters are investigated. Specifically, M-estimators are applied to define Robust Multivariate Process Capability Indices (RMPCIs), offering a more resilient alternative to traditional approaches. Simulation results and real-world applications confirm that RMPCIs outperform classical MPCIs under outlier contamination, ensuring more accurate and consistent quality assessments. The study demonstrates the practical value of RMPCIs in industrial environments and suggests further exploration of alternative robust estimators.