A New Bootstrap Based Algorithm for Hotelling’s T2 Multivariate Control Chart

Bootstrap-Based T2 Multivariate Control Charts

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop u...

Multivariate Multinomial T2 Control Chart using Fuzzy Approach

On the multivariate variation control chart

Multivariate control charts such as Hotelling`s T^ 2 and X^ 2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in ...

Mann - Withney multivariate nonparametric control chart.

In many quality control applications, the necessary distributional assumptions to correctly apply the traditional parametric control charts are either not met or there is simply not enough information or evidence to verify the assumptions. It is well known that performance of many parametric control charts can be seriously degraded in situations like this. Thus, control charts that do not requi...

A bootstrap control chart for Birnbaum-Saunders percentiles

The problem of detecting a shift in the percentile of a Birnbaum–Saunders population in a process monitoring situation is considered. For example, such problems may arise when the quality characteristic of interest is tensile strength or breaking stress. The parametric bootstrap method is used to develop a quality control chart for monitoring percentiles when process measurements have a Birnbau...