Stanford University - Department of Statistics
Scan statistics are a common tool to detect e.g. spatial disease clusters or to describe local differences between two distributions. Multivariate scan statistics pose both a statistical problem due to the multiple testing over many scan windows, as well as a computational problem because statistics have to be evaluated on many windows. I will describe methodology that leads to both statistically optimal inference and computationally efficient algorithms.
If time permits, I will discuss average likelihood ratio statistics, which have recently been proposed as a competitor to scan statistics, with claims of superior performance.