A Simple Robust Control Chart Based on MAD

A Robust Dispersion Control Chart Based on M-estimate

Process control charts are proven techniques for improving quality. Specifying the control limits is the most important step in designing a control chart. The presence of outliers may extremely affect the estimates of parameters using classical methods. Robust estimators which are not affected by outliers or the small departures from the model assumptions are applied in this paper to specify th...

Robust multivariate control chart based on goodness-of-fit test

This paper proposes a distribution-free multivariate statistical process control (MSPC) chart to detect general distributional changes in multivariate process variables. The chart is deployed based on a multivariate goodness-of-fit test, which is extensible to high dimensional observations. The chart also employs data-dependent control limits, which are computed on line along with the charting ...

Robust Holt-Winter based control chart for monitoring autocorrelated simple linear pro les with contaminated data

Robust Economic-Statistical Design of Acceptance Control Chart

Acceptance control charts (ACC), as an effective tool for monitoring highly capable processes, establish control limits based on specification limits when the fluctuation of the process mean is permitted or inevitable. For designing these charts by minimizing economic costs subject to statistical constraints, an economic-statistical model is developed in this paper. However, the parameters of s...

A Robust Control Chart for Monitoring Dispersion

Most robust control charts in the literature are for monitoring process location parameters, such as mean or median, rather than process dispersion parameters.This paper develops a new robust control chart by integrating a two-sample nonparametric test into the effective change-point model. Our proposed chart is easy in computation, convenient to use, and very powerful in detecting process disp...