Finite sample change point inference and identification for high-dimensional mean vectors

Keyphrases

• Finite Sample 100%
• Bootstrap 100%
• High-dimensional Mean 100%
• Mean Vector 100%
• Cumulative Sum Test 100%
• Cumulative Sum Statistics 100%
• Convergence Rate 66%
• Separation Condition 33%
• Simulation Study 33%
• Change Point Detection 33%
• Non-asymptotic 33%
• Signal Strength 33%
• Covariance 33%
• Strength Condition 33%
• High Dimension 33%
• Test Statistic 33%
• Data Dependency 33%
• Dynamically Adjust 33%
• Finite Sample Performance 33%
• Moment Conditions 33%
• Point Location 33%
• Cumulative Sum 33%
• Detection Rules 33%
• Dependence Structure 33%
• Signal Separation 33%
• Multiple Change-points 33%
• Gaussian multiplier Bootstrap 33%
• Uniform Validity 33%
• Arbitrary Dependence 33%
• Sparse Alternatives 33%
• Binary Segmentation Algorithm 33%
• Generalized Cumulative Sum 33%
• Minimax Separation Rates 33%
• Mean-shift Model 33%

Mathematics

• Cumulative Sum 100%
• Simulation Study 14%
• Gaussian Distribution 14%
• Asymptotics 14%
• Minimax 14%
• Covariance 14%
• Higher Dimensions 14%
• Test Statistic 14%
• Independent Sample 14%
• Null 14%
• Point Location 14%
• Moment Condition 14%
• Dependence Structure 14%
• Changepoint Detection 14%
• Dependent Data 14%
• Mean Shift 14%