Change-point inference for high-dimensional heteroscedastic data
Author:
Affiliation:
1. Microsoft Corporation Redmond
2. Department of Statistics University of Toronto
3. Department of Statistics University of Illinois a Urbana-Champaign
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability,Statistics, Probability and Uncertainty
Reference34 articles.
1. Brillinger, D. (1975). Time Series: Data Analysis and Theory. Holden-Day, San Francisco.
2. Bücher, A. and I. Kojadinovic (2019). A note on conditional versus joint unconditional weak convergence in bootstrap consistency results. Journal of Theoretical Probability 32(3), 1145–1165.
3. Chen, S. X. and Y.-L. Qin (2010). A two-sample test for high-dimensional data with applications to gene-set testing. The Annals of Statistics 38(2), 808–835.
4. Cho, H. and P. Fryzlewicz (2015). Multiple-change-point detection for high dimensional time series via sparsified binary segmentation. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77(2), 475–507.
5. Dehling, H. and T. Mikosch (1994). Random quadratic forms and the bootstrap for u-statistics. Journal of Multivariate Analysis 41, 392–413.
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