Detecting clusters with increased mean using scan windows with variable radius

Abstract

Applying spatiotemporal scan statistics is an effective method to detect the clustering of mean shifts in many application fields. Although several exponentially weighted moving average (EWMA) based scan statistics have been proposed, the existing methods generally require a fixed scan window size or apply the weighting technique across the temporal axis only. However, the size of shift coverage is often unavailable in practical problems. Using a mismatching scan radius may mislead the size of cluster coverage in space or delay the time to detection. This research proposed an stEWMA method by applying the weighting technique across both temporal and spatial axes with variable scan radius. The simulation analysis showed that the stEWMA method can have a significantly shorter time to detection than the likelihood ratio-based scan statistic using variable scan radius, especially when cluster coverage size is small. The application to detecting the increase of male thyroid cancer in the New Mexico state also showed the effectiveness of the proposed method.