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C. Lowry, D. Montgomery (1995)
A review of multivariate control chartsIie Transactions, 27
C. Wright, David Booth, M. Hu (2001)
Joint Estimation: SPC Method for Short-Run Autocorrelated DataJournal of Quality Technology, 33
D. Hawkins (1993)
Regression Adjustment for Variables in Multivariate Quality ControlJournal of Quality Technology, 25
Cuihua Zhang, Haibin Yu, Xiao-yuan Huang (2009)
Quality control strategy in supply chain under asymmetric informationInternational Journal of Operational Research, 4
Xia Pan, J. Jarrett (2013)
Using Golden Ratio Search to Improve Paired Construction of Quality Control ChartsInternational Journal of Education and Management Engineering, 3
Xia Pan, J. Jarrett (2004)
Applying State Space to SPC: Monitoring Multivariate Time SeriesJournal of Applied Statistics, 31
J. Jackson (1985)
Multivariate quality controlCommunications in Statistics-theory and Methods, 14
D. Hawkins (1991)
Multivariate quality control based on regression-adjusted variablesQuality Engineering, 36
Layth Alwan (1999)
Statistical Process Analysis
S. Wierda (1994)
Multivariate statistical process control—recent results and directions for future researchStatistica Neerlandica, 48
P. Berthouex, W. Hunter, L. Pallesen (1978)
Monitoring Sewage Treatment Plants: Some Quality Control AspectsJournal of Quality Technology, 10
H. Maragah, W. Woodall (1992)
The effect of autocorrelation on the retrospective X-chartJournal of Statistical Computation and Simulation, 40
Layth Alwan (1992)
Effects of autocorrelation on control chart performanceCommunications in Statistics-theory and Methods, 21
M. Testik (2005)
Model Inadequacy and Residuals Control Charts for Autocorrelated ProcessesQuality and Reliability Engineering International, 21
H. Hotelling (1947)
Multivariate Quality Control
Don Wardell, H. Moskowitz, R. Plante (1994)
[Run-Length Distributions of Special-Cause Control Charts for Correlated Processes]: RejoinderTechnometrics, 36
Chung Chen, Lon-Mu Liu (1993)
Joint Estimation of Model Parameters and Outlier Effects in Time SeriesJournal of the American Statistical Association, 88
W. Woodall (2006)
The Use of Control Charts in Health-Care and Public-Health SurveillanceJournal of Quality Technology, 38
C. Sonesson, David Bock (2003)
A review and discussion of prospective statistical surveillance in public healthJournal of the Royal Statistical Society: Series A (Statistics in Society), 166
G. Box, A. Luceño (1997)
Statistical Control: By Monitoring and Feedback Adjustment
Marcel Dekker, A. Kalagonda, S. Kulkarni (2003)
Diagnosis of Multivariate Control Chart Signal Based on Dummy Variable Regression TechniqueCommunications in Statistics - Theory and Methods, 32
Nola Tracy, John Young, R. Mason (1992)
Multivariate Control Charts for Individual ObservationsJournal of Quality Technology, 24
J. Jackson (1959)
Quality Control Methods for Several Related VariablesTechnometrics, 1
J. English, T. Sastri (1990)
Enhanced quality control in continuous flow processes, 19
Ih Chang, G. Tiao, Chung Chen (1988)
Estimation of time series parameters in the presence of outliersTechnometrics, 30
Don Wardell, H. Moskowitz, R. Plante (1994)
Run-Length Distributions of Special-Cause Control Charts for Correlated ProcessesTechnometrics, 36
J. Jarrett, Xia Pan (2007)
The quality control chart for monitoring multivariate autocorrelated processesComput. Stat. Data Anal., 51
A. Yeh, Longcheen Huwang, Yunlong Wu (2004)
A likelihood-ratio-based EWMA control chart for monitoring variability of multivariate normal processesIIE Transactions, 36
A. Kalgonda, S. Kulkarni (2004)
Multivariate Quality Control Chart for Autocorrelated ProcessesJournal of Applied Statistics, 31
Chung Chen, Lon-Mu Liu (1993)
Forecasting time series with outliersJournal of Forecasting, 12
Wade Molnau, D. Montgomery, G. Runger (2001)
Statistically constrained economic design of the multivariate exponentially weighted moving average control chartQuality and Reliability Engineering International, 17
Layth Alwan, D. Radson (1992)
TIME-SERIES INVESTIGATION OF SUBSAMPLE MEAN CHARTSIie Transactions, 24
J. Coleman, J. Nickerson (2005)
A multivariate exponentially weighted moving average control chart for photovoltaic processesConference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 2005.
J. Hunter (1986)
The exponentially weighted moving averageJournal of Quality Technology, 18
C. Mastrangelo, David Forrest (2002)
Multivariate Autocorrelated Processes: Data and Shift GenerationJournal of Quality Technology, 34
O. Atienza, L. Tang, B. Ang (1998)
A SPC Procedure for Detecting Level Shifts of Autocorrelated ProcessesJournal of Quality Technology, 30
Chao-Wen Lu, M. Reynolds (1999)
Control Charts for Monitoring the Mean and Variance of Autocorrelated ProcessesJournal of Quality Technology, 31
Xia Pan, J. Jarrett (2007)
Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlationInternational Journal of Production Economics, 106
J. Jarrett, Xia Pan (2007)
Monitoring Variability and Analyzing Multivariate Autocorrelated ProcessesJournal of Applied Statistics, 34
Su-Fen Yang, M. Rahim (2005)
Economic statistical process control for multivariate quality characteristics under Weibull shock modelInternational Journal of Production Economics, 98
R. Boyles (2000)
Phase I Analysis for Autocorrelated ProcessesJournal of Quality Technology, 32
G. Box, G. Tiao (1975)
Intervention Analysis with Applications to Economic and Environmental ProblemsJournal of the American Statistical Association, 70
A. Papaioannou, A. Mavridou, C. Hadjichristodoulou, Panagiotis Papastergiou, O. Pappa, Eleni Dovriki, Ioannis Rigas (2010)
Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public healthEnvironmental Monitoring and Assessment, 170
A. Papaioannou, Eleni Dovriki, N. Rigas, P. Plageras, Ioannis Rigas, M. Kokkora, Panagiotis Papastergiou (2010)
Assessment and Modelling of Groundwater Quality Data by Environmetric Methods in the Context of Public HealthWater Resources Management, 24
S. Novak (2011)
Extreme Value Methods with Applications to Finance
E. Elsayed, Hao Zhang (2007)
Design of Optimum Simple Step-Stress Accelerated Life Testing Plans
D. West, S. Dellana, J. Jarrett (2002)
Transfer Function Modeling of Processes with Dynamic InputsJournal of Quality Technology, 34
J. Benneyan (2001)
Number-Between g-Type Statistical Quality Control Charts for Monitoring Adverse EventsHealth Care Management Science, 4
T. Harris, W. Ross (1991)
Statistical process control procedures for correlated observationsCanadian Journal of Chemical Engineering, 69
Purpose – The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health services. In particular, the authors examine the construction and use of industrial quality control methods as applied to the public providers, in both the prevention and cure for infectious diseases and the quality of public health care providers in such applications including water quality standards, sewage many others. The authors suggest implementing modern multivariate applications of quality control techniques and/or better methods for univariate quality control common in industrial applications in the public health sector to both control and continuously improve public health services. These methods entitled total quality management (TQM) form the foundation to improve these public services. Design/methodology/approach – The study is designed to indicate the great need for TQM analysis to utilize methods of statistical quality control. All this is done to improve public health services through implementation of quality control and improvement methods as part of the TQM program. Examples of its use indicate that multivariate methods may be the best but other methods are suggested as well. Findings – Multivariate methods provide the best solutions when quality and reliability tests show indications that the variables observed are inter-correlated and correlated over time. Simpler methods are available when the above factors are not present. Research limitations/implications – Multivariate methods will provide for better interpretation of results, better decisions and smaller risks of both Type I and Type II errors. Smaller risks lead to better decision making and may reduce costs. Practical implications – Analysts will improve such things as the control of water quality and all aspects of public health when data are collected through experimentation and/or periodic quality management techniques. Social implications – Public health will be better monitored and the quality of life will improve for all especially in places where public development is undertaking rapid changes. Originality/value – The manuscript is original because it uses well known and scientific methods of analyzing data in area where data collection is utilized to improve public health.
International Journal of Quality & Reliability Management – Emerald Publishing
Published: Jan 4, 2016
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