English

Chevet-type inequalities for subexponential Weibull variables and estimates for norms of random matrices

Probability 2025-02-05 v2 Functional Analysis

Abstract

We prove two-sided Chevet-type inequalities for independent symmetric Weibull random variables with shape parameter r[1,2]r\in[1,2]. We apply them to provide two-sided estimates for operator norms from pn\ell_p^n to qm\ell_q^m of random matrices (aibjXi,j)im,jn(a_ib_jX_{i,j})_{i\le m, j\le n}, in the case when Xi,jX_{i,j}'s are iid symmetric Weibull variables with shape parameter r[1,2]r\in[1,2] or when XX is an isotropic log-concave unconditional random matrix. We also show how these Chevet-type inequalities imply two-sided bounds for maximal norms from pn\ell_p^n to qm\ell_q^m of submatrices of XX in both Weibull and log-concave settings.

Keywords

Cite

@article{arxiv.2309.04214,
  title  = {Chevet-type inequalities for subexponential Weibull variables and estimates for norms of random matrices},
  author = {Rafał Latała and Marta Strzelecka},
  journal= {arXiv preprint arXiv:2309.04214},
  year   = {2025}
}

Comments

17 pages. Corollary 3, Theorem 4, Corollary 5, Corollaries 10 & 12 and their proofs added, Conjecture 13 and Remark 14 added. Title and introduction changed