Kernel Test

Kernel tests are statistical methods used to assess relationships between variables, particularly in scenarios with complex, non-linear dependencies, often employing kernel methods to map data into higher-dimensional spaces where linear methods can be applied. Current research focuses on improving the efficiency and power of these tests, particularly through the development of novel kernel combinations, efficient algorithms (like those based on incomplete U-statistics and coreset compression), and addressing challenges in high-dimensional data. These advancements enhance the ability to detect subtle relationships in diverse fields, including genomics, neuroimaging, and single-cell analysis, leading to more powerful and reliable inferences from complex datasets.