Probability inequalities for convex sets and multidimensional concentration functions
AbstractThis paper derives a sharp bound for the probability that a sum of independent symmetric random vectors lies in a symmetric convex set. In one dimension this bound is an improvement of an inequality first proved by Kolmogorov. The subject of multidimensional concentration functions is also treated.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 6 (1976)
Issue (Month): 2 (June)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Mattner, Lutz & Roos, Bero, 2008. "Maximal probabilities of convolution powers of discrete uniform distributions," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2992-2996, December.
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