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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 asymptotic null distributions. To this end, we develop new tools and techniques to establish the asymptotic distribution of the maximum-type statistic under a more relaxed condition on componentwise correlations among all variables than those in existing literature. The proposed method is simple to use. It is adaptive to different levels of the sparsity of change signals, and is comparable to or even outperforms existing approaches as revealed by our numerical studies.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkad048","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T04:24:00Z","timestamp":1684470240000},"page":"936-958","source":"Crossref","is-referenced-by-count":12,"title":["Computationally efficient and data-adaptive changepoint inference in high dimension"],"prefix":"10.1093","volume":"85","author":[{"given":"Guanghui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Statistics, Academy of Statistics and Interdisciplinary Sciences, and KLATASDS-MOE, East China Normal University , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Statistics and Data Science, KLMDASR, LEBPS, and LPMC, Nankai University , Tianjin , 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