Highest Posterior Distribution (HPD) Control Chart for Individual Observation

Distribution-free control chart for Bivariate Process

V.B. Ghute Dept. of Statistics, Solapur University, Solapur, (MS), India [email protected]; +91 9881372729 ______________________________________________________________________________________________ Abstract Nonparametric or distribution-free control chart is useful in statistical process control when the underlying process distribution is unknown or is not likely to be normal. In...

A Distribution-Free Multivariate Control Chart

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This paper develops a new nonparametric methodology for monitoring location parame...

Supplementary Materials for “A Distribution-Free Multivariate Control Chart”

A Bayesian Approach for the Recognition of Control Chart Patterns

In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...

Bayesian Mixture Labelling by Highest Posterior Density

A fundamental problem for Bayesian mixture model analysis is label switching, which occurs due to the non-identifiability of the mixture components under symmetric priors. We propose two labelling methods to solve this problem. The first method, denoted by PM(ALG), is based on the posterior modes and an ascending algorithm generically denoted ALG. We use each Markov chain Monte Carlo (MCMC) sam...