{ "id": "0906.3886", "version": "v2", "published": "2009-06-21T18:06:25.000Z", "updated": "2011-06-17T05:18:33.000Z", "title": "Concentration of measures via size biased couplings", "authors": [ "Subhankar Ghosh", "Larry Goldstein" ], "comment": "Concentration results for the number of isolated vertices have been removed from this version, and with corrections, posted jointly with Martin Raic in http://arxiv.org/abs/1106.0048", "categories": [ "math.PR" ], "abstract": "Let $Y$ be a nonnegative random variable with mean $\\mu$ and finite positive variance $\\sigma^2$, and let $Y^s$, defined on the same space as $Y$, have the $Y$ size biased distribution, that is, the distribution characterized by E[Yf(Y)]=\\mu E f(Y^s) for all functions $f$ for which these expectations exist. Under a variety of conditions on the coupling of Y and $Y^s$, including combinations of boundedness and monotonicity, concentration of measure inequalities hold. Examples include the number of relatively ordered subsequences of a random permutation, sliding window statistics including the number of m-runs in a sequence of coin tosses, the number of local maximum of a random function on a lattice, the number of urns containing exactly one ball in an urn allocation model, the volume covered by the union of $n$ balls placed uniformly over a volume n subset of d dimensional Euclidean space, the number of bulbs switched on at the terminal time in the so called lightbulb process, and the infinitely divisible and compound Poisson distributions that satisfy a bounded moment generating function condition.", "revisions": [ { "version": "v2", "updated": "2011-06-17T05:18:33.000Z" } ], "analyses": { "subjects": [ "60E15", "60C05" ], "keywords": [ "size biased couplings", "concentration", "compound poisson distributions", "dimensional euclidean space", "urn allocation model" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2009arXiv0906.3886G" } } }