A0936
Title: Random interval distillation for detecting multiple changes in dependent dynamic networks
Authors: Xinyuan Fan - Tsinghua University (China)
Weichi Wu - Tsinghua University (China) [presenting]
Abstract: The aim is to propose a new and generic approach for detecting multiple change points in general dependent data, termed random interval distillation (RID). By collecting random intervals with sufficient strength of signals and reassembling them into a sequence of informative short intervals, the new approach captures the shifts in signal characteristics across diverse dependent data forms, including locally stationary high-dimensional time series and dynamic networks with Markov formation. For practical applications, a clustering-based and data-driven procedure is introduced to determine the optimal threshold for signal strength, which is adaptable to a wide array of dependent data scenarios utilizing the connection between RID and clustering. The focus is on the application of RID to dependent dynamic networks with a secondary refinement tailored to it to enhance localization precision. Notably, for low-rank autoregressive networks, the methods achieve the minimax optimality as their independent counterparts. The effectiveness and usefulness of the methodology are examined via extensive simulation studies and a real data example, implementing it in the R-package rid.
