This is an announcement for the paper "L_p moments of random vectors via majorizing measures" by Olivier Guedon and Mark Rudelson.
Abstract: For a random vector X in R^n, we obtain bounds on the size of a sample, for which the empirical p-th moments of linear functionals are close to the exact ones uniformly on a given convex body K. We prove an estimate for a general random vector and apply it to several problems arising in geometric functional analysis. In particular, we find a short Lewis type decomposition for any finite dimensional subspace of L_p and study in detail the case of an isotropic log-concave random vector. We also prove a concentration estimate for the empirical moments. The main ingredient of the proof is the construction of an appropriate majorizing measure to bound a certain Gaussian process.
Archive classification: Functional Analysis
Mathematics Subject Classification: 46B09, 52A21
Remarks: 33 pages
The source file(s), gr05-06-16.tex: 72987 bytes, is(are) stored in gzipped form as 0507023.gz with size 21kb. The corresponding postcript file has gzipped size 110kb.
Submitted from: rudelson@math.missouri.edu
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