This paper develops a unified, accurate and computationally efficient method
for change-point inference in non-stationary spatio-temporal processes. By
modeling a non-stationary spatio-temporal process as a piecewise stationary
spatio-temporal process, we consider simultaneous estimation of the number and
locations of change-points, and model parameters in each segment. A composite
likelihood-based criterion is developed for change-point and parameters
estimation. Asymptotic theories including consistency and distribution of the
estimators are derived under mild conditions. In contrast to classical results
in fixed dimensional time series that the asymptotic error of change-point
estimator is Op​(1), exact recovery of true change-points is guaranteed in
the spatio-temporal setting. More surprisingly, the consistency of change-point
estimation can be achieved without any penalty term in the criterion function.
A computational efficient pruned dynamic programming algorithm is developed for
the challenging criterion optimization problem. Simulation studies and an
application to U.S. precipitation data are provided to demonstrate the
effectiveness and practicality of the proposed method