Title: Dynamic tracking and screening in massive data streams
Authors: Lilun Du - HKUST (Hong Kong) [presenting]
Abstract: The aim is to construct a large-scale dynamic tracking and screening (DTS) procedure capable of rapidly identifying irregular individual streams whose behavioral patterns deviate from that of the majority. By fully exploiting the sequential feature of datastreams, we first develop a robust estimation approach under a framework of varying coefficient model. The procedure naturally accommodates unequally-spaced design points and updates estimates as new data arrive without the need to store an ever-increasing data history. A data-driven choice of an optimal smoothing parameter is accordingly proposed. Then, we suggest a new model-specification test tailored to the streaming environment. The resulting DTS scheme is able to adapt time-varying structures appropriately, track changes in the underlying models, and hence maintain high identification accuracy in detecting irregular individuals. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real data example.