Nonparametric control charts are useful in statistical process control (SPC) when there is a lack of or limited knowledge about the underlying process distribution, especially when the process measurement is multivariate. This paper develops a new multivariate self-starting methodology for monitoring location parameters. It is based on adapting the multivariate spatial rank to on-line sequentia...

Identifying convergence in numerical optimization is an ever-present, difficult, and often subjective task. The statistical framework of Gaussian process surrogate model optimization provides useful measures for tracking optimization progress; however, the identification of convergence via these criteria has often provided only limited success and often requires a more subjective analysis. Here...

Control charts based on generalized likelihood ratio test (GLRT) are attractive from both theoretical and practical points of view.Most of the existingworks in the literature focusing on the detection of the process mean and variance are almost based on the assumption that the shifts remain constant over time. The case of the patterned mean and variance changes may not be well discussed. In thi...

Most commonly used control charts for monitoring quality characteristics of the processes were developed under the assumption that the observations are randomly sampled from a normal population. It is well known that these control charts have more false alarms than usual when processes are positively autocorrelated. One remedy is to adjust the control limits such that the modified control chart...

BACKGROUND Comparison of outcomes requires adequate risk adjustment for differences in patient risk and the type of intervention performed. Both unintentional and intentional misclassification (also called gaming) of risk factors might lead to incorrect benchmark results. Therefore, misclassification of risk factors should be detected. We investigated the use of statistical process control tech...

BACKGROUND In most clinical monitoring cases there is a need to track more than one quality characteristic. If separate univariate charts are used, the overall probability of a false alarm may be inflated since correlation between variables is ignored. In such cases, multivariate control charts should be considered. PURPOSE This paper considers the implementation and performance of the T(2), ...