Scan Statistic

Scan statistics are powerful tools used to detect anomalous patterns or change points within data, finding applications across diverse fields like epidemiology, image analysis, and network analysis. Current research focuses on improving the accuracy and efficiency of scan statistic methods, particularly through the development and refinement of non-parametric approaches and the integration of machine learning techniques, such as convolutional neural networks (CNNs) and deep learning architectures like Res-UNet+. These advancements address challenges like miscalibration in existing methods and improve the precision of anomaly detection in complex datasets, leading to more reliable insights in various scientific and practical domains.