Exact Asymptotics for the Scan Statistic and Fast Alternatives

TL;DR

This paper derives exact asymptotics for various scan statistics used in detecting rectangular activations in noisy grid data, providing theoretical insights and fast algorithms with near-linear runtime.

Contribution

It introduces exact asymptotic analysis for multiple scan statistic variants and proposes a near-linear time approximation with comparable power.

Findings

Exact asymptotic level and power for four scan statistic variants

Development of a near-linear time epsilon-net approximation

Numerical experiments validating theoretical results

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 (Kabluchko, 2011) under the null hypothesis, using non-constant boundary crossing probabilities for locally-stationary Gaussian random fields derived in (Chan and Lai, 2006). 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; an adaptive variant; and an epsilon-net approximation to the latter, in the spirit of (Arias-Castro, 2005). This approximate scan runs in time near-linear in the size of the grid and achieves the same asymptotic power as the adaptive scan. We complement our theory with some numerical experiments.