Introduces SS-SN for hypothesis testing of functional parameters in time series, deriving pivotal limiting distributions under null hypotheses and power functions under local alternatives for applications including CDF testing, time-reversibility, and spectral change points.
arXiv preprint arXiv:2002.06633 , year=
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Compares seeded and random intervals for change point segmentation and introduces a novel noise level estimator that improves model selection in frequent change point scenarios with low signal-to-noise ratios.
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Hypothesis Testing for a Functional Parameter via Self-normalization
Introduces SS-SN for hypothesis testing of functional parameters in time series, deriving pivotal limiting distributions under null hypotheses and power functions under local alternatives for applications including CDF testing, time-reversibility, and spectral change points.
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Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
Compares seeded and random intervals for change point segmentation and introduces a novel noise level estimator that improves model selection in frequent change point scenarios with low signal-to-noise ratios.