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Max-Chart for Autocorrelated Processes
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K. Thaga
Published/Copyright:
March 10, 2010
Abstract
Statistical process control procedures are usually implemented under the assumption that the observations from a process are independent over time. However, this assumption is often violated. Therefore, we propose a single Shewhart-type control chart for autocorrelated process by fitting a time series model into the process and monitoring the residuals from the forecast values of a fitted time series model. Numerical results illustrate the ARL of the AR(1) plus random error model, for the cases of step changes in the mean and/or standard deviation. Compared to other charts that monitor autocorrelated processes, this chart quickly detects shifts in the process location and spread particularly for large shifts.
Published Online: 2010-03-10
Published in Print: 2007-April
© Heldermann Verlag
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Articles in the same Issue
- Bivariate Density Classification by the Geometry of the Marginals
- Improving the Variability Function in Case of a Uni-Modal Probability Distribution
- New Results in Economic Statistical Quality Control
- Reliability Analysis Based on Scalar Fuzzy Variables
- A Bayesian Approach to Parallel Stress-Strength Models
- Max-Chart for Autocorrelated Processes
- A Note on Classes of Lifetime Distributions
- Sampling Risks for Chain Sampling Plans with Non-Constant Defective Probability
- Effects of Measurement Error on Controlling Two Dependent Process Steps
- GERT Analysis of m-consecutive-k-out-of-n:F Systems with Dependence
