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Control Charts Based on the g-and-h Distribution
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Das Nandini
Veröffentlicht/Copyright:
15. März 2011
Abstract
Control charts are the most popular tool to detect the occurrence of an assignable cause in a production process. Traditional control charts are based on the approximation with the normal distribution. In many practical situation, however, assuming normality is not adequate. Under these situations, the use of traditional control chart may lead to erroneous decisions. For handling non-normal process distributions one may use non-parametric control charts, however these methods are rather inefficient. Another approach is to use a generalized distribution. In this work the univariate g-and-h distribution is used to approximate non-normal process distributions.
Received: 2010-09-14
Published Online: 2011-03-15
Published in Print: 2011-September
© de Gruyter 2011
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Artikel in diesem Heft
- Editorial
- Control Charts Based on the g-and-h Distribution
- Economic Reliability Group Acceptance Sampling Plans Based on the Inverse-Rayleigh and the Log-Logistic Distributions
- Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
- One-Sided Cumulative Sum (CUSUM) Control Charts for the Zero-Truncated Binomial Distribution
- Significance Test for the Half Logistic Distribution
- The Quality Loss Index QLI and Its Properties
- Statistical Quality Control Limits for the Sample Mean Chart Using Robust Extreme Ranked Set Sampling
Schlagwörter für diesen Artikel
Generalized Control Chart;
g-and-h Distribution;
Non-Normal Process Distribution
Artikel in diesem Heft
- Editorial
- Control Charts Based on the g-and-h Distribution
- Economic Reliability Group Acceptance Sampling Plans Based on the Inverse-Rayleigh and the Log-Logistic Distributions
- Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
- One-Sided Cumulative Sum (CUSUM) Control Charts for the Zero-Truncated Binomial Distribution
- Significance Test for the Half Logistic Distribution
- The Quality Loss Index QLI and Its Properties
- Statistical Quality Control Limits for the Sample Mean Chart Using Robust Extreme Ranked Set Sampling
