A tuning-parameter-free self-normalized test detects changes in the marginal distribution of object-valued time series under weak dependence, with first nonparametric consistency results for multiple change-point estimation via wild binary segmentation.
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A hyperparameter-free angular kernel scan framework detects marginal distributional shifts in HDLSS regimes with theoretical guarantees under cross-coordinate mixing.
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Change-Point Detection for Object-valued Time Series
A tuning-parameter-free self-normalized test detects changes in the marginal distribution of object-valued time series under weak dependence, with first nonparametric consistency results for multiple change-point estimation via wild binary segmentation.
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High-Dimensional Change-Point Detection via Angular Kernel Statistics
A hyperparameter-free angular kernel scan framework detects marginal distributional shifts in HDLSS regimes with theoretical guarantees under cross-coordinate mixing.