Develops a bivariate partial sum process and self-normalized CUSUM test for detecting mean changes in locally stationary time series, with proven weak convergence and asymptotic level/consistency properties.
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Monte Carlo approximation of selective p-values in changepoint post-selection inference that conditions on less to improve power while remaining valid for any sample size.
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Self-Normalization for CUSUM-based Change Detection in Locally Stationary Time Series
Develops a bivariate partial sum process and self-normalized CUSUM test for detecting mean changes in locally stationary time series, with proven weak convergence and asymptotic level/consistency properties.
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Improving Power by Conditioning on Less in Post-selection Inference for Changepoints
Monte Carlo approximation of selective p-values in changepoint post-selection inference that conditions on less to improve power while remaining valid for any sample size.