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We present an efficient score statistic, called the $\textsf{S}^3 \textsf{T}$statistic, to detect the emergence of a spatially and temporally correlatedsignal from either fixed-sample or sequential data. The signal may cause a menshift and/or a change in the covariance structure. The score statistic cancapture both spatial and temporal structures of the change and hence isparticularly powerful in detecting weak signals. The score statistic iscomputationally efficient and statistically powerful. Our main theoreticalcontribution are accurate analytical approximations on the false alarm rate ofthe detection procedures, which can be used to calibrate the thresholdanalytically. Numerical experiments on simulated and real data demonstrate thegood performance of our procedure for solar flame detection and water qualitymonitoring.

$\textsf{S}^3T$: An Efficient Score-Statistic for Spatio-Temporal Surveillance