Joint Monitoring of Process Location and Dispersion Based on CUSUM Procedure and Generalized Likelihood Ratio in the Presence of Measurement Errors

2017 ◽  
Vol 33 (7) ◽  
pp. 1485-1498
Author(s):  
Mohammad Reza Maleki ◽  
Ali Salmasnia
Keyword(s):  
Likelihood Ratio ◽  
Measurement Errors ◽  
Generalized Likelihood Ratio ◽  
Cusum Procedure ◽  
Joint Monitoring ◽  
Process Location

scholarly journals Sequential Generalized Likelihood Ratio Tests for Online Item Monitoring

2021 ◽  
Author(s):  
Hyeon-Ah Kang
Keyword(s):  
Likelihood Ratio ◽  
Empirical Evaluation ◽  
Error Rates ◽  
Likelihood Ratio Tests ◽  
Superior Performance ◽  
Generalized Likelihood Ratio ◽  
Item Parameters ◽  
Sufficient Power ◽  
Multiple Item ◽  
Joint Monitoring

The study presents statistical procedures that monitor functioning of items over time. We propose generalized likelihood ratio tests that surveil multiple item parameters and implement with various sampling techniques to perform continuous or intermittent monitoring. The procedures examine stability of item parameters across time and inform compromise as soon as they identify significant parameter shift. The performance of the monitoring procedures was validated using simulated and real assessment data. The empirical evaluation suggests that the proposed procedures perform adequately well in identifying the parameter drift. They showed satisfactory detection power and gave timely signals while regulating the error rates reasonably low. The procedures also showed superior performance when compared with the existent methods. The empirical findings suggest that multivariate parametric monitoring can provide an efficient and powerful control tool for maintaining the quality of items. The procedures allow joint monitoring of multiple item parameters and achieve sufficient power by dint of likelihood-ratio tests. Based on the findings from the empirical experimentation, we suggest some practical strategies for performing online item monitoring.

Fault Detection in Linear Discrete Dynamic Systems by a Pattern Recognition of a Generalized-Likelihood-Ratio

1990 ◽  
Vol 112 (2) ◽  
pp. 276-282 ◽  
Author(s):  
S. Tanaka ◽  
P. C. Mu¨ller
Keyword(s):  
Pattern Recognition ◽  
Likelihood Ratio ◽  
Dynamic Systems ◽  
Abrupt Change ◽  
Detection Method ◽  
Discrete Dynamical System ◽  
Generalized Likelihood Ratio ◽  
Robust Detection ◽  
Chi Squared ◽  
Discrete Dynamic Systems

The detection of an abrupt change in the parameters of a linear discrete dynamical system is considered in the framework of the easily implemented generalized-likelihood-ratio (GLR) method. This paper proposes a robust detection method based on a pattern recognition of the maximum GLR provided by the conventional step-hypothesized GLR method. A numerical example demonstrates that the proposed method is highly superior to the conventional step-hypothesized GLR method and to the Chi-squared test in both detection rate and detection speed.

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