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

TL;DR

The paper introduces the $ extsf{S}^3 extsf{T}$ score statistic, a computationally efficient and powerful method for detecting spatially and temporally correlated signals in fixed or sequential data, with applications in environmental monitoring.

Contribution

It proposes a novel score statistic that captures both spatial and temporal changes, with analytical false alarm rate approximations for calibration.

Findings

Effective in detecting weak signals in simulated data.

Demonstrates good performance in real-world applications.

Provides analytical tools for threshold setting.

Abstract

We present an efficient score statistic, called the $\textsf{S}^3 \textsf{T}$ statistic, to detect the emergence of a spatially and temporally correlated signal from either fixed-sample or sequential data. The signal may cause a men shift and/or a change in the covariance structure. The score statistic can capture both spatial and temporal structures of the change and hence is particularly powerful in detecting weak signals. The score statistic is computationally efficient and statistically powerful. Our main theoretical contribution are accurate analytical approximations on the false alarm rate of the detection procedures, which can be used to calibrate the threshold analytically. Numerical experiments on simulated and real data demonstrate the good performance of our procedure for solar flame detection and water quality monitoring.