Exact asymptotics for the scan statistic and fast alternatives

Abstract

We consider the problem of detecting a rectangle of activation in a grid of sensors in $d$-dimensions with noisy measurements. This has applications to massive surveillance projects and anomaly detection in large datasets in which one detects anomalously high measurements over rectangular regions, or more generally, blobs. Recently, the asymptotic distribution of a multiscale scan statistic was established in [18] under the null hypothesis, using non-constant boundary crossing probabilities for locally-stationary Gaussian random fields derived in [8]. Using a similar approach, we derive the exact asymptotic level and power of four variants of the scan statistic: an oracle scan that knows the dimensions of the activation rectangle; the multiscale scan statistic just mentioned; the adaptive variant; and an $\epsilon $-net approximation to the latter, in the spirit of [3]. This approximate scan runs in time near-linear in the size of the grid and achieves the same asymptotic level and power as the adaptive scan, and has a poly-logarithmic time parallel implementation. We complement our theory with some numerical experiments, and make some practical recommendations.