A Locally Adaptive Algorithm for Multiple Testing with Network Structure

Understand

Incorporating auxiliary information alongside primary data can significantly enhance the accuracy of simultaneous inference.

• However, existing multiple testing methods face challenges in efficiently incorporating complex side information, especially when it differs in dimension or structure from the primary data, such as network side information.
• This paper introduces a locally adaptive structure learning algorithm (LASLA), a flexible framework designed to integrate a broad range of auxiliary information into the inference process.
• Although LASLA is specifically motivated by the challenges posed by network-structured data, it also proves highly effective with other types of side information, such as spatial locations and multiple auxiliary sequences.