Electronic Communications in Probability· 2013Q2
Hanson-Wright inequality and sub-gaussian concentration
- 546citations
- Q2SCImago
- 2013year
Short summary
A modern proof of the Hanson-Wright inequality for quadratic forms in sub-gaussian random variables yields a new concentration inequality for sub-gaussian random vectors.
AI-generated from the title and abstract; the full text is not read.
Key points
- Presents a modern proof of the Hanson-Wright inequality for quadratic forms in sub-gaussian random variables.
- Deduces a new concentration inequality for sub-gaussian random vectors.
- Illustrates applications with examples on random vector-subspace distances and matrix norms.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this expository note, we give a modern proof of Hanson-Wright inequality for quadratic forms in sub-gaussian random variables.We deduce a useful concentration inequality for sub-gaussian random vectors.Two examples are given to illustrate these results: a concentration of distances between random vectors and subspaces, and a bound on the norms of products of random and deterministic matrices.
The authors' abstract, as published at the source. Electronic Communications in Probability, 2013 · DOI ↗
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Field: Applied Mathematics
Applied MathematicsMathematics