On-Line Monitoring When the Process Yields a Linear Profile

Kang, Lan; Albin, Susan L.

doi:10.1080/00224065.2000.11980027

Cited by 480 publications

(435 citation statements)

References 18 publications

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“…Charnes (1995) studies the situation where the processes under surveillance are time dependent and where the residuals (or forecasting errors) are used in the monitoring. Kang and Albin (2000) model the variable Y as a function of the variable X and monitor the slope and intercept by T2 statistic as well as the residuals (deviations from reference line). Aparisi et al (2001) reduce the covariance matrix through the determinant or the trace.…”

Section: Suggested Approaches For Multivariate Surveillancementioning

confidence: 99%

Effect of Dependency in Systems for Multivariate Surveillance

Andersson

2009

Communications in Statistics - Simulation and Computation

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In many situations we need a system for detecting changes early. Examples are early detection of disease outbreaks, of patients at risk and of financial instability. In influenza outbreaks, for example, we want to detect an increase in the number of cases. Important indicators might be the number of cases of influenza-like illness and pharmacy sales (e.g. aspirin). By continually monitoring these indicators, we can early detect a change in the process of interest. The methodology of statistical surveillance is used. Often, the conclusions about the process(es) of interest is improved if the surveillance is based on several indicators. Here three systems for multivariate surveillance are compared. One system, called LRpar, is based on parallel likelihood ratio methods, since the likelihood ratio has been shown to have several optimality properties. In LRpar, the marginal density of each indicator is monitored and an alarm is called as soon as one of the likelihood ratios exceeds its alarm limit. The LRpar is compared to an optimal alarm system, called LRjoint, which is derived from the full likelihood ratio for the joint density. The performances of LRpar and LRjoint are compared to a system where the Hotellings T2 is monitored. The evaluation is made using the delay of a motivated alarm, as a function of the times of the changes. The effect of dependency is investigated: both dependency between the monitored processes and correlation between the time points when the changes occur. When the first change occurs immediately, the three methods work rather similarly, for independent processes and zero correlation between the change times. But when all processes change later, the T2 has much longer delay than LRjoint and LRpar. This holds both when the processes are independent and when they have a positive covariance. When we assume a positive correlation between the change times, the LRjoint yields a shorter delay than LRpar when the changes actually do occur simultaneously, whereas the opposite is true when the changes do actually occur at different time point.

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Section: Suggested Approaches For Multivariate Surveillancementioning

confidence: 99%

Effect of Dependency in Systems for Multivariate Surveillance

Andersson

2009

Communications in Statistics - Simulation and Computation

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“…We are concerned with ongoing monitoring to detect assignable causes in the process in phase II controlling. Useful recognitions of phase I and phase II applications have been studied already, for example, by Kang and Albin (2000), Woodall (2000), Hawkins et al (2003), Woodall et al (2004), Montgomery (2005), and Jensen et al (2006).…”

Section: Introductionmentioning

confidence: 99%

A New Procedure to Monitor the Mean of a Quality Characteristic

Kiani

Panaretos

Psarakis

2008

Communications in Statistics - Simulation and Computation

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“…However in the discrete approaches, we may found the setting which leads to minimum deviations by considering selected signal levels. For better comprehension, consider the semiconductor manufacturing problem from Kang and Albin [29]. A critical device in this process is mass flow controller (MFC) which controls the flow of gases in the chamber.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

2017

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In some processes, quality of a product should be characterized by functional relationships between response variables and a signal factor. Hence the traditional methods cannot be used to find the optimum solution. In this paper, we propose a method which considers two different dispersion effects, i.e. in domain and between replicates variations in the functional responses. Besides, we propose an integral based measure to find the deviation from target function. A probabilistic method is applied to consider the correlation structure of functional responses. Three numerical examples and a real case from literature are studied to show the efficiency of the proposed method.

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On-Line Monitoring When the Process Yields a Linear Profile

Cited by 480 publications

References 18 publications

Effect of Dependency in Systems for Multivariate Surveillance

Effect of Dependency in Systems for Multivariate Surveillance

A New Procedure to Monitor the Mean of a Quality Characteristic

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

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