Title: Adaptive changepoint inference in high dimension
Description: Implements the double-max-sum (DMS) method for adaptive changepoint inference in high dimension, as proposed by Wang and Feng (2023). Additionally, it offers various other adaptive testing approaches, including Liu, Zhang, Zhang and Liu (2020) and Zhang, Wang and Shao (2022), as well as non-adaptive ones, such as Jirak (2015), Yu and Chen (2021), and Wang, Zou, Wang and Yin (2019).
You can install the development version of DMS from GitHub with:
# install.packages("devtools")
devtools::install_github("ghwang-nk/DMS")library(DMS)
?DMS
?LZZL20
?ZWS22
?J15
?max_2sample
?YC21
?WZWY19- Jirak, M. (2015) Uniform change point tests in high dimension. The Annals of Statistics, 43, 2451–2483.
- Liu, B., Zhou, C., Zhang, X. and Liu, Y. (2020) A Unified Data-Adaptive Framework for High Dimensional Change Point Detection. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82, 933–963.
- Wang, G. and Feng, L. (2023) Computationally efficient and data-adaptive changepoint inference in high dimension. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85, 936–958.
- Wang, Y., Zou, C., Wang, Z. and Yin, G. (2019) Multiple change-points detection in high dimension. Random Matrices: Theory and Applications, 08, 1950014.
- Yu, M. and Chen, X. (2021) Finite Sample Change Point Inference and Identification for High-Dimensional Mean Vectors. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83, 247–270.
- Zhang, Y., Wang, R. and Shao, X. (2022) Adaptive Inference for Change Points in High-Dimensional Data. Journal of the American Statistical Association, 117, 1751–1762.
We would like to express our sincere gratitude to Professor Xiaofeng Shao for his invaluable assistance in helping us implement the ZWS22 test, as introduced by Zhang, Wang and Shao (2022). The core implementation of the test is based on an adapted version of their Matlab code. For those interested, the original Matlab code can be accessed through this link: https://doi.org/10.1080/01621459.2021.1884562.