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Register a new userA Note on Distribution Free Symmetrization Inequalities
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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).
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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 is always superior. In this paper, we present some background on these issues, provide an example where Cheeger beats Poincare, establish some sufficient conditions on the canonical paths for the Poincare bound to triumph, and show that there is always a choice of paths for which this happens.
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 exists for all positive d. In this paper a relation between central and non-central multivariate gamma distributions is shown, which implies the existence of the p-variate gamma distribution at least for all non-integer d greater than the integer part of (p-1)/2 without any additional assumptions for the associated covariance matrix.
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 $g_i$ are independent standard Gaussian variables and $A_i$ are matrix coefficients. This bound exhibits a logarithmic dependence on dimension that is sharp when the matrices $A_i$ commute, but often proves to be suboptimal in the presence of noncommutativity. In this paper, we develop nonasymptotic bounds on the spectrum of arbitrary Gaussian random matrices that can capture noncommutativity. These bounds quantify the degree to which the deterministic matrices $A_i$ behave as though they are freely independent. This intrinsic freeness phenomenon provides a powerful tool for the study of various questions that are outside the reach of classical methods of random matrix theory. Our nonasymptotic bounds are easily applicable in concrete situations, and yield sharp results in examples where the noncommutative Khintchine inequality is suboptimal. When combined with a linearization argument, our bounds imply strong asymptotic freeness (in the sense of Haagerup-Thorbj{o}rnsen) for a remarkably general class of Gaussian random matrix models, including matrices that may be very sparse and that lack any special symmetries. Beyond the Gaussian setting, we develop matrix concentration inequalities that capture noncommutativity for general sums of independent random matrices, which arise in many problems of pure and applied mathematics.
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.
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