Monitoring multivariate process variability with individual observations via penalised likelihood estimation

Monitoring multivariate process variability with individual observations via penalised likelihood estimation

Excessive variation in a manufacturing process is one of the major causes of a high defect rate and poor product quality. Therefore, quick detection of changes, especially increases in process variability, is essential for quality control. In modern manufacturing environments, most of the quality characteristics that have to be closely monitored are multivariate by the nature of the application...

Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, f...

Covariance selection and estimation via penalised normal likelihood

We propose a nonparametric method to identify parsimony and to produce a statistically efficient estimator of a large covariance matrix. We reparameterise a covariance matrix through the modified Cholesky decomposition of its inverse or the one-step-ahead predictive representation of the vector of responses and reduce the nonintuitive task of modelling covariance matrices to the familiar task o...

Maximum likelihood estimation for multivariate mixture observations of markov chains

Simultaneous Monitoring of Multivariate-Attribute Process Mean and Variability Using Artificial Neural Networks

In some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. In this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribu...