  High-dimensional changepoint inference that adapts to various change patterns
has received much attention recently. We propose a simple, fast yet effective
approach for adaptive changepoint testing. The key observation is that two
statistics based on aggregating cumulative sum statistics over all dimensions
and possible changepoints by taking their maximum and summation, respectively,
are asymptotically independent under some mild conditions. Hence we are able to
form a new test by combining the p-values of the maximum- and summation-type
statistics according to their limit null distributions. To this end, we develop
new tools and techniques to establish asymptotic distribution of the
maximum-type statistic under a more relaxed condition on componentwise
correlations among all variables than that in existing literature. The proposed
method is simple to use and computationally efficient. It is adaptive to
different sparsity levels of change signals, and is comparable to or even
outperforms existing approaches as revealed by our numerical studies.
