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Multivariate statistical process control charts are often used for process monitoring to detect out-of-control anomalies. However, multivariate control charts based on conventional statistical distance measures, such as the one used in the Hotelling's T 2 control chart, cannot scale up to large amounts of complex process data, e.g. data with a large number(More)
Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T 2 , cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square (χ 2) Distance Monitoring (CSDM) procedure for monitoring large-scale, complex(More)
Optimal experimental design practices are prominent in many applications. This paper proposes an alternate way of computing the information matrix, a key consideration in planning an accelerated life test. The generalized linear model approach allows optimal designs to be computed using iteratively weighted least-square solutions versus a maximum likelihood(More)