AI Chat AI Image Generator AI Video Text to Speech

A Note on High-Dimensional Confidence Regions

05/19/2021

∙

by Sven Klaassen, et al.

∙

∙

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.

research

∙ 05/19/2022

High-dimensional Data Bootstrap

This article reviews recent progress in high-dimensional bootstrap. We f...

0 Victor Chernozhukov, et al. ∙

research

∙ 10/30/2015

A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations

We propose a new inferential framework for constructing confidence regio...

0 Matey Neykov, et al. ∙

research

∙ 12/30/2014

A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models

We consider the problem of uncertainty assessment for low dimensional co...

0 Yang Ning, et al. ∙

research

∙ 12/31/2017

Confidence set for group membership

This paper develops procedures for computing a confidence set for a late...

0 Andreas Dzemski, et al. ∙

research

∙ 06/28/2017

Asymptotic Confidence Regions for High-dimensional Structured Sparsity

In the setting of high-dimensional linear regression models, we propose ...

0 Benjamin Stucky, et al. ∙

research

∙ 02/16/2022

Geometry of the Minimum Volume Confidence Sets

Computation of confidence sets is central to data science and machine le...

0 Heguang Lin, et al. ∙

research

∙ 02/01/2022

Bootstrap Confidence Regions for Learned Feature Embeddings

Algorithmic feature learners provide high-dimensional vector representat...

0 Kris Sankaran, et al. ∙