Adaptive Inference for Change Points in High-Dimensional Data
暂无分享,去创建一个
[1] Arjun K. Gupta,et al. Parametric Statistical Change Point Analysis , 2000 .
[2] A. Rinaldo,et al. Optimal change point detection and localization in sparse dynamic networks , 2018, The Annals of Statistics.
[3] Hao Chen,et al. Graph-based change-point detection , 2012, 1209.1625.
[4] Kengo Kato,et al. Central limit theorems and bootstrap in high dimensions , 2014, 1412.3661.
[5] Diane J. Cook,et al. A survey of methods for time series change point detection , 2017, Knowledge and Information Systems.
[6] Wei Biao Wu,et al. LOCAL WHITTLE ESTIMATION OF FRACTIONAL INTEGRATION FOR NONLINEAR PROCESSES , 2007, Econometric Theory.
[7] L. Horváth,et al. Change‐point detection in panel data , 2012 .
[8] A. Aue,et al. Structural breaks in time series , 2013 .
[9] Farida Enikeeva,et al. High-dimensional change-point detection with sparse alternatives , 2013, 1312.1900.
[10] M. Jirak. Uniform change point tests in high dimension , 2015, 1511.05333.
[11] W. Wu,et al. Nonlinear system theory: another look at dependence. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[12] Hongzhe Li,et al. Optimal Sparse Segment Identification With Application in Copy Number Variation Analysis , 2010, Journal of the American Statistical Association.
[13] M. Cugmas,et al. On comparing partitions , 2015 .
[14] X. Shao,et al. Testing for Change Points in Time Series , 2010 .
[15] Tengyao Wang,et al. High dimensional change point estimation via sparse projection , 2016, 1606.06246.
[16] Lizhen Lin,et al. Change-point detection in dynamic networks via graphon estimation , 2019, 1908.01823.
[17] Kengo Kato,et al. Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors , 2013 .
[18] Ignacio N. Lobato. Testing That a Dependent Process Is Uncorrelated , 2001 .
[19] Wei Pan,et al. An adaptive two-sample test for high-dimensional means , 2016, Biometrika.
[20] Nancy R. Zhang,et al. Detecting simultaneous changepoints in multiple sequences. , 2010, Biometrika.
[21] Song-xi Chen,et al. A two-sample test for high-dimensional data with applications to gene-set testing , 2010, 1002.4547.
[22] William M. Rand,et al. Objective Criteria for the Evaluation of Clustering Methods , 1971 .
[23] I. G. Zhurbenko,et al. On higher spectral densities of stationary processes with mixing , 1975 .
[24] L. Horváth,et al. Limit Theorems in Change-Point Analysis , 1997 .
[25] David Siegmund,et al. MODEL SELECTION FOR HIGH-DIMENSIONAL, MULTI-SEQUENCE CHANGE-POINT PROBLEMS , 2012 .
[26] Piotr Fryzlewicz,et al. Wild binary segmentation for multiple change-point detection , 2014, 1411.0858.
[27] Wei Biao Wu,et al. Limit theorems for iterated random functions , 2004, Journal of Applied Probability.
[28] Camille Roth,et al. Natural Scales in Geographical Patterns , 2017, Scientific Reports.
[29] X. Shao,et al. A self‐normalized approach to confidence interval construction in time series , 2010, 1005.2137.
[30] Haeran Cho,et al. Change-point detection in panel data via double CUSUM statistic , 2016, 1611.08631.
[31] Chao Gao,et al. Minimax rates in sparse, high-dimensional change point detection , 2019, The Annals of Statistics.
[32] X. Shao,et al. Self-Normalization for Time Series: A Review of Recent Developments , 2015 .
[33] Xiaohui Chen,et al. Finite sample change point inference and identification for high‐dimensional mean vectors , 2017, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[34] Pierre Perron,et al. Dealing with Structural Breaks , 2005 .
[35] D. Andrews. Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation , 1991 .
[36] L. Horváth,et al. Darling–Erdős limit results for change-point detection in panel data , 2013 .
[37] W. Pan,et al. ASYMPTOTICALLY INDEPENDENT U-STATISTICS IN HIGH-DIMENSIONAL TESTING. , 2018, Annals of statistics.
[38] X. Shao,et al. Hypothesis testing for high-dimensional time series via self-normalization , 2020 .
[39] Ting Zhang,et al. Unsupervised Self-Normalized Change-Point Testing for Time Series , 2018 .