Measure concentration for Euclidean distance in the case of dependent random variables
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Katalin Marton [1]
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[1] Hungarian Academy of Sciences
Hungarian Academy of Sciences
Hungría
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- Localización: Annals of probability: An official journal of the Institute of Mathematical Statistics, ISSN 0091-1798, Vol. 32, Nº. 3, 2, 2004, págs. 2526-2544
- Idioma: inglés
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Resumen
Let qn be a continuous density function in n-dimensional Euclidean space. We think of qn as the density function of some random sequence Xn with values in $\Bbb{R}^{n}$. For I⊂[1,n], let XI denote the collection of coordinates Xi, i∈I, and let $\widebar X_{I}$ denote the collection of coordinates Xi, i∉I. We denote by $Q_{I}(x_{I}|\bar{x}_{I})$ the joint conditional density function of XI, given $\widebar X_{I}$. We prove measure concentration for qn in the case when, for an appropriate class of sets I, (i) the conditional densities $Q_{I}(x_{I}|\bar{x}_{I})$, as functions of xI, uniformly satisfy a logarithmic Sobolev inequality and (ii) these conditional densities also satisfy a contractivity condition related to Dobrushin and Shlosman’s strong mixing condition.
