共查询到3条相似文献,搜索用时 0 毫秒
1.
Shin-Li Lu 《统计学通讯:理论与方法》2013,42(11):3344-3356
ABSTRACTA generally weighted moving average (GWMA) control chart with fast initial response (FIR) features is addressed to monitor an autoregressive process mean shift. Numerical simulations based on average run length (ARL) show that the GWMA control chart with additional FIR feature requires less time to detect small or moderate shifts than GWMA control chart at low level of autocorrelation; whereas these two control charts perform similarly at high level of autocorrelation. Regardless of any level of autocorrelation, GWMA control charts provided with additional FIR feature have a good performance in detecting large shifts during the initial stage. 相似文献
2.
Wen-Chih Chiu 《Journal of applied statistics》2009,36(3):255-275
The generally weighted moving average (GWMA) control chart is an extension model of exponentially weighted moving average (EWMA) control chart. Recently, some approaches have been proposed to modify EWMA charts with fast initial response (FIR) features. We introduce these approaches in GWMA-type charts. Via simulation, various control schemes are designed and then their average run lengths are computed and compared. Based on the overall performance, it is showed that the DGWMA chart is the best choice especially when the shift is moderate, and the GWMA charts provided with additional FIR feature have a good performance only in detecting large shifts during the initial stage. 相似文献
3.
Manabu Kuroki 《Journal of applied statistics》2012,39(3):673-694
In the case where non-experimental data are available from an industrial process and a directed graph for how various factors affect a response variable is known based on a substantive understanding of the process, we consider a problem in which a control plan involving multiple treatment variables is conducted in order to bring a response variable close to a target value with variation reduction. Using statistical causal analysis with linear (recursive and non-recursive) structural equation models, we configure an optimal control plan involving multiple treatment variables through causal parameters. Based on the formulation, we clarify the causal mechanism for how the variance of a response variable changes when the control plan is conducted. The results enable us to evaluate the effect of a control plan on the variance of a response variable from non-experimental data and provide a new application of linear structural equation models to engineering science. 相似文献