  High-dimensional self-exciting point processes have been widely used in many
application areas to model discrete event data in which past and current events
affect the likelihood of future events. In this paper, we are concerned with
detecting abrupt changes of the coefficient matrices in discrete-time
high-dimensional self-exciting Poisson processes, which have yet to be studied
in the existing literature due to both theoretical and computational challenges
rooted in the non-stationary and high-dimensional nature of the underlying
process. We propose a penalized dynamic programming approach which is supported
by a theoretical rate analysis and numerical evidence.
