ﻻ يوجد ملخص باللغة العربية
Let $X, Y$ be two independent identically distributed (i.i.d.) random variables taking values from a separable Banach space $(mathcal{X}, |cdot|)$. Given two measurable subsets $F, Ksubseteqcal{X}$, we established distribution free comparison inequalities between $mathbb{P}(Xpm Y in F)$ and $mathbb{P}(X-Yin K)$. These estimates are optimal for real random variables as well as when $mathcal{X}=mathbb{R}^d$ is equipped with the $|cdot|_infty$ norm. Our approach for both problems extends techniques developed by Schultze and Weizsacher (2007).
This is an auxiliary note to [12]. To be precise, here we have gathered the proofs of all the statements in [12, Section 5] that happen to have points of contact with techniques recently developed in Chousionis-Pratt [5] and Chunaev [6].
In 1991, Persi Diaconis and Daniel Stroock obtained two canonical path bounds on the second largest eigenvalue for simple random walk on a connected graph, the Poincare and Cheeger bounds, and they raised the question as to whether the Poincare bound
The p-variate gamma distribution in the sense of Krishnamoorthy and Parthasarathy exists for all positive integer degrees of freedom d and at least for all real values d > p-2, p > 1. For special structures of the associated covariance matrix it also
A central tool in the study of nonhomogeneous random matrices, the noncommutative Khintchine inequality of Lust-Piquard and Pisier, yields a nonasymptotic bound on the spectral norm of general Gaussian random matrices $X=sum_i g_i A_i$ where $g_i$ ar
We give an elementary probabilistic proof of Veraverbekes Theorem for the asymptotic distribution of the maximum of a random walk with negative drift and heavy-tailed increments. The proof gives insight into the principle that the maximum is in general attained through a single large jump.