Introduces AJDN for consistent jump recovery and localization under asynchronicity and nonstationarity, plus AJDN-H for group-based trend estimation in high-dimensional piecewise locally stationary series.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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stat.ME 2years
2024 2verdicts
UNVERDICTED 2representative citing papers
Sparsity of regression parameters or differential parameters is not necessary for consistent multiple change point detection in high-dimensional linear regression; a covariance discrepancy scan is statistically and computationally more efficient.
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Complex trend inference for high-dimensional piecewise locally stationary time series
Introduces AJDN for consistent jump recovery and localization under asynchronicity and nonstationarity, plus AJDN-H for group-based trend estimation in high-dimensional piecewise locally stationary series.
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Detection and inference of changes in high-dimensional linear regression with non-sparse structures
Sparsity of regression parameters or differential parameters is not necessary for consistent multiple change point detection in high-dimensional linear regression; a covariance discrepancy scan is statistically and computationally more efficient.