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A Note on High-Dimensional Confidence Regions

05/19/2021

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by Sven Klaassen, et al.

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Recent advances in statistics introduced versions of the central limit theorem for high-dimensional vectors, allowing for the construction of confidence regions for high-dimensional parameters. In this note, s-sparsely convex high-dimensional confidence regions are compared with respect to their volume. Specific confidence regions which are based on ℓ_p-balls are found to have exponentially smaller volume than the corresponding hypercube. The theoretical results are validated by a comprehensive simulation study.

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