Multiple auxiliary information-based (MAIB) memory-type $t$ charts are proposed with fixed and variable sampling intervals for an improved monitoring of the process mean, which include adaptive/nonadaptive cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) $t$ charts. These control charts are constructed based on a unique uniformly minimum variance unbiased estimator of the process mean that requires information on a study variable as well as on several correlated auxiliary variables. The Monte Carlo simulation technique is used to compute the run length characteristics of the proposed charts when sampling from a multivariate normal distribution. The run length comparisons show that the proposed MAIB-$t$ charts outperform their existing auxiliary information based (AIB) and non-AIB $t$ charts, where the normalizing transformation is used for all considered $t$ charts in order to have uniformity in the comparisons. A real data application is also given to support the proposed theory.

中文翻译：

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具有多个过程均值辅助信息的记忆型 t 图

多重辅助信息型 (MAIB) 记忆型 $吨$ 提出了具有固定和可变采样间隔的图表，以改进对过程均值的监控，其中包括自适应/非自适应累积和 (CUSUM) 和指数加权移动平均 (EWMA) $吨$图表。这些控制图是基于过程均值的唯一一致最小方差无偏估计量构建的，该估计量需要有关研究变量以及几个相关辅助变量的信息。Monte Carlo 模拟技术用于计算从多元正态分布采样时所建议图表的运行长度特征。运行长度比较表明，建议的 MAIB-$吨$ 图表优于现有的基于辅助信息的 (AIB) 和非 AIB $吨$ 图表，其中归一化转换用于所有考虑 $吨$图表，以便在比较中具有一致性。还给出了一个真实的数据应用来支持所提出的理论。