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Regina Liu, Jen Tang (1996)
Control Charts for Dependent and Independent Measurements Based on Bootstrap MethodsJournal of the American Statistical Association, 91
A. Gandy, J. Kvaløy (2011)
Guaranteed Conditional Performance of Control Charts via Bootstrap MethodsScandinavian Journal of Statistics, 40
Snigdhansu Chatterjee, P. Qiu (2009)
Distribution-free cumulative sum control charts using bootstrap-based control limitsarXiv: Applications
Y. Lio, Chanseok Park (2008)
A bootstrap control chart for Birnbaum–Saunders percentilesQuality and Reliability Engineering International, 24
W. Levinson, D. Holmes, A. Mergen (2002)
Variation Charts for Multivariate ProcessesQuality Engineering, 14
Kaibo Wang, A. Yeh, Bo Li (2014)
Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimationComput. Stat. Data Anal., 78
Edward Furman (2007)
On a Multivariate Gamma Distribution
M. Khoo, S. Quah (2004)
Alternatives to the Multivariate Control Chart for Process DispersionQuality Engineering, 16
L. Jones‐Farmer, W. Woodall, S. Steiner, Charles Champ (2014)
An Overview of Phase I Analysis for Process Improvement and MonitoringJournal of Quality Technology, 46
Hyo-Il Park (2009)
Median Control Charts Based on Bootstrap MethodCommunications in Statistics - Simulation and Computation, 38
Mahmoud Mahmoud, P. Maravelakis (2010)
The Performance of the MEWMA Control Chart when Parameters are EstimatedCommunications in Statistics - Simulation and Computation, 39
F. Alt, N. Smith (1988)
17 Multivariate process controlHandbook of Statistics, 7
Nesma Saleh, Mahmoud Mahmoud, Abdel-Salam Abdel-Salam (2013)
The Performance of the Adaptive Exponentially Weighted Moving Average Control Chart with Estimated ParametersQuality and Reliability Engineering International, 29
A. Mostajeran, N. Iranpanah, R. Noorossana (2016)
A New Bootstrap Based Algorithm for Hotelling’s T2 Multivariate Control Chartjournal of sciences islamic republic of iran, 27
Poovich Phaladiganon, S. Kim, V. Chen, Wei Jiang (2013)
Principal component analysis-based control charts for multivariate nonnormal distributionsExpert Syst. Appl., 40
T. Seppälä, H. Moskowitz, R. Plante, Jen Tang (1995)
Statistical Process Control via the Subgroup BootstrapJournal of Quality Technology, 27
V. Ghute, D. Shirke (2008)
A Multivariate Synthetic Control Chart for Process DispersionQuality Technology & Quantitative Management, 5
Edgard Maboudou-Tchao, Vincent Agboto (2013)
Monitoring the covariance matrix with fewer observations than variablesComput. Stat. Data Anal., 64
Shing Chang, Kui Zhang (2007)
Statistical Process Control for Variance Shift Detections of Multivariate Autocorrelated ProcessesQuality Technology & Quantitative Management, 4
F. Aparisi, J. Jabaloyes, A. Carrión (2001)
GENERALIZED VARIANCE CHART DESIGN WITH ADAPTIVE SAMPLE SIZES. THE BIVARIATE CASECommunications in Statistics - Simulation and Computation, 30
A. Yeh, Longcheen Huwang, Chien-Wei Wu (2005)
A multivariate EWMA control chart for monitoring process variability with individual observationsIIE Transactions, 37
J. Surtihadi, M. Raghavachari, G. Runger (2004)
Multivariate control charts for process dispersionInternational Journal of Production Research, 42
R. Arellano-Valle, H. Bolfarine, V. Lachos (2005)
Skew-normal Linear Mixed ModelsJournal of Data Science
A. Grigoryan, D. He (2005)
Multivariate double sampling |S| charts for controlling process variabilityInternational Journal of Production Research, 43
DC Montgomery (2009)
Introduction to statistical quality control
A. Polansky (2005)
A General Framework for Constructing Control ChartsQuality and Reliability Engineering International, 21
C. Kramer, D. Jensen (1969)
Fundamentals of Multivariate Analysis Part II. Inference About Two TreatmentsJournal of Quality Technology, 1
B. Korin (1968)
On the distribution of a statistic used for testing a covariance matrix.Biometrika, 55 1
Willis Jensen, L. Jones‐Farmer, Charles Champ, W. Woodall (2006)
Effects of Parameter Estimation on Control Chart Properties: A Literature ReviewJournal of Quality Technology, 38
A. Saghir, Zhengyan Lin (2014)
A Study on the Robustness of G-Chart to Non-NormalityCommunications in Statistics - Simulation and Computation, 43
DM Hawkins, EM Maboudou‐Tchao (2008)
Multivariate exponentially weighted moving covariance matrix, 50
A. Yeh, D. Lin, Hongsheng Zhou, C. Venkataramani (2003)
A multivariate exponentially weighted moving average control chart for monitoring process variabilityJournal of Applied Statistics, 30
J. Diaz (2007)
The 'effective variance' control chart for monitoring the dispersion process with missing dataEuropean Journal of Industrial Engineering, 1
M. Riaz, Aamir Saghirr (2007)
Monitoring Process Variability Using Gini’s Mean DifferenceQuality Technology & Quantitative Management, 4
A. Azzalini, A. Valle (1996)
The multivariate skew-normal distributionBiometrika, 83
P. Tang, N. Barnett (1996)
DISPERSION CONTROL FOR MULTIVARIATE PROCESSESAustralian & New Zealand Journal of Statistics, 38
Mahmoud Mahmoud, P. Maravelakis (2013)
The performance of multivariate CUSUM control charts with estimated parametersJournal of Statistical Computation and Simulation, 83
S. Psarakis, Angeliki Vyniou, P. Castagliola (2014)
Some Recent Developments on the Effects of Parameter Estimation on Control ChartsQuality and Reliability Engineering International, 30
Min Zhang, Yahui Xu, Zhen He, Xuejun Hou (2015)
The Effect of Estimation Error on Risk‐Adjusted Survival Time CUSUM Chart PerformanceQuality and Reliability Engineering International, 32
R. Noorossana, Sepehr Fathizadan, M. Nayebpour (2016)
EWMA Control Chart Performance with Estimated Parameters under Non‐normalityQuality and Reliability Engineering International, 32
Bo Li, Kaibo Wang, A. Yeh (2013)
Monitoring the covariance matrix via penalized likelihood estimationIIE Transactions, 45
Poovich Phaladiganon, S. Kim, V. Chen, Jun-Geol Baek, Sun-Kyoung Park (2011)
Bootstrap-Based T 2 Multivariate Control ChartsCommunications in Statistics - Simulation and Computation, 40
Edgard Maboudou-Tchao, N. Diawara (2013)
A LASSO Chart for Monitoring the Covariance MatrixQuality Technology & Quantitative Management, 10
Various charts such as |S|, W, and G are used for monitoring process dispersion. Most of these charts are based on the normality assumption, while exact distribution of the control statistic is unknown, and thus limiting distribution of control statistic is employed which is applicable for large sample sizes. In practice, the normality assumption of distribution might be violated, while it is not always possible to collect large sample size. Furthermore, to use control charts in practice, the in‐control state usually has to be estimated. Such estimation has a negative effect on the performance of control chart. Non‐parametric bootstrap control charts can be considered as an alternative when the distribution is unknown or a collection of large sample size is not possible or the process parameters are estimated from a Phase I data set. In this paper, non‐parametric bootstrap multivariate control charts |S|, W, and G are introduced, and their performances are compared against Shewhart‐type control charts. The proposed method is based on bootstrapping the data used for estimating the in‐control state. Simulation results show satisfactory performance for the bootstrap control charts. Ultimately, the proposed control charts are applied to a real case study.
Applied Stochastic Models in Business and Industry – Wiley
Published: Jan 1, 2017
Keywords: ; ; ; ; ; ;
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