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The Poisson INAR(1) one-sided EWMA chart with estimated parameters

机译:具有估计参数的Poisson INAR(1)单面EWMA图

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The Poisson INAR(1) one-sided exponentially weighted moving average (EWMA) chart has been proposed to monitor integer-valued autoregressive processes of order 1 with a Poisson marginal distribution. It is common to assume that the INAR(1) process parameters are known or can be accurately estimated. However, in practice, the in-control process mean and autocorrelation coefficient are typically unknown and must be estimated. In this article, we investigate the effect of parameter estimation on the run length properties of the Poisson INAR(1) one-sided EWMA chart with the use of bivariate Markov chain approach. It is shown from the conditional in-control and out-of-control average run length values with different design parameters under various shift magnitudes that the effect of parameter estimation error can be significant. The effect due to the process mean estimation error is stronger than that due to the autocorrelation coefficient. Moreover, practitioners should rarely expect the in-control performance to be close to that obtained under the assumption that process parameters are known. We also provide sample size-recommendations regarding marginal performance. This sample size depends on the different shift magnitudes.
机译:已经提出了Poisson INAR(1)单边指数加权移动平均值(EWMA)图表来监视具有Poisson边际分布的1阶整数值自回归过程。通常假定INAR(1)过程参数是已知的或可以准确估计的。但是,实际上,控制中过程的平均值和自相关系数通常是未知的,必须进行估计。在本文中,我们使用双变量马尔可夫链方法研究参数估计对Poisson INAR(1)单面EWMA图的游程长度属性的影响。从在不同的移位量下具有不同设计参数的条件内控制和失控平均行程长度值可以看出,参数估计误差的影响可能很大。由于过程平均估计误差引起的影响要强于由于自相关系数引起的影响。此外,从业人员很少应该期望控制性能接近在已知过程参数的假设下获得的性能。我们还提供有关边际绩效的样本量建议。该样本大小取决于不同的移位幅度。

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