EcoSta 2018: Registration
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Title: On-line monitoring data quality of high-dimensional data streams Authors:  Zhonghua Li - Nankai University (China) [presenting]
Abstract: In recent years, effective monitoring of data quality has increasingly attracted attention of researchers in the area of statistical quality control. Among the relevant research on this topic, none used multivariate methods to control the multidimensional data quality process, but instead relied on multiple univariate control charts. Based on a novel one-sided multivariate exponentially weighted moving average (MEWMA) chart, a conditional false discovery rate (FDR) adjusted scheme will be introduced to on-line monitor the data quality of high-dimensional data streams. With thousands of input data streams, the average run length loses its usefulness because one will likely have out-of-control signals at each time period. Hence, the FDR and power are chosen as two criteria used for the performance comparison. Numerical results show that the proposed MEWMA scheme has both less conservative FDR and high average power.