Online Monitoring of High-Dimensional Data Streams Based on Conditional FDR
In traditional methods for monitoring high-dimensional data streams using control charts,alarms are often triggered constantly,even when there is no significant change.In the case of high-dimensional data streams with inherent correlations and the flexibility of data entering and exiting over time,a robust approach for online monitoring is required.This paper introduces the concept of Conditional False Discovery Rate(FDR)to address this issue,proposing a method for robustly detecting shifts in the mean of high-dimensional data streams.For non-normal high-dimensional data streams,the method involves partitioning and aggregating the samples,and then constructing monitoring statistics that satisfy symmetry by combining Sample Data Aggregation(SDA)with Exponentially Weighted Moving Average(EWMA)techniques.Dynamic thresholds for online monitoring are determined through the Conditional FDR approach.An AR(1)model is employed to capture the correlation structure between data streams.The performance of the proposed method is evaluated through Monte Carlo simulations,focusing on the trade-off between false discovery rate and power.Numerical simulation results indicate that the proposed method achieves satisfactory monitoring performance,providing a robust and effective solution for online monitoring of high-dimensional data streams.
high-dimensional data flowfalse discovery ratesymmetric data aggregationstatistical process control
万方数据
维普
