Detecting Changes in the Second Moment Structure of High-Dimensional Sensor-Type Data in a $K$-Sample Setting

The $K$ sample problem for high-dimensional vector time series is studied, especially focusing on sensor data streams, in order to analyze the second moment structure and detect changes across samples and/or across variables cumulated sum (CUSUM) statistics of bilinear forms of the sample covariance matrix. In this model $K$ independent vector time series $\mathbf{Y}_{T,1},\dots,\mathbf{Y}_{T,K}$ are observed over a time span $[0,T]$, which may correspond to $K$ sensors (locations) yielding $d$-dimensional data as well as $K$ locations where $d$ sensors emit univariate data. Unequal sample sizes are considered as arising when the sampling rate of the sensors differs. We provide large sample approximations and two related change-point statistics, a sums of squares and a pooled variance statistic. The resulting procedures are investigated by simulations and illustrated by analyzing a real data set.

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