Moving blocks bootstrap control chart for dependent multivariate data

2005 ◽  
Author(s):  
Yu-ming Liu ◽  
Jun Liang ◽  
Ji-xin Qian
Keyword(s):  
Control Chart ◽  
Multivariate Data ◽  
Moving Blocks Bootstrap

scholarly journals Robust Monitoring of Contaminated Multivariate Data

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Eric B. Howington
Keyword(s):  
Control Chart ◽  
Phase Ii ◽  
Multivariate Data ◽  
False Alarms ◽  
Multivariate Processes ◽  
Contaminated Data ◽  
Work Stoppage ◽  
Data Contamination ◽  
Number Of False Alarms ◽  
Excessive Number

Monitoring a process that suffers from data contamination using a traditional multivariate T2 chart can lead to an excessive number of false alarms. A diagnostic statistic can be used to distinguish between real control chart signals due to assignable causes and signals due to contamination from a single outlier. In phase II analysis, a traditional T2 control chart augmented by a diagnostic statistic improves the work stoppage rates for multivariate processes suffering from contaminated data and maintains the ability to detect process shifts.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

New control chart for multivariate data with missing values

1988 ◽  
Vol 21 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Hiroshi Furutani ◽  
Koji Yamamoto ◽  
Hisakazu Ogura ◽  
Yasuhiro Kitazoe
Keyword(s):  
Control Chart ◽  
Missing Values ◽  
Multivariate Data

Man-Computer Derivations of Tree Structure from Multivariate Data

1968 ◽  
Author(s):  
Gerald H. Shure ◽  
Laurence I. Press ◽  
Miles S. Rogers
Keyword(s):  
Multivariate Data ◽  
Tree Structure
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