Estimates for the strong approximation in multidimensional central limit theorem

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dc.creatorZaitsev, A. Yu.
dc.date2003-04-24
dc.date.accessioned2026-07-07T04:57:19Z
dc.date.available2026-07-07T04:57:19Z
dc.descriptionIn a recent paper the author obtained optimal bounds for the strong Gaussian approximation of sums of independent $\R^d$-valued random vectors with finite exponential moments. The results may be considered as generalizations of well-known results of Komlós--Major--Tusnády and Sakhanenko. The dependence of constants on the dimension $d$ and on distributions of summands is given explicitly. Some related problems are discussed.
dc.identifierhttps://arxiv.org/abs/math/0304373
dc.identifierhttp://arxiv.org/abs/math/0304373
dc.identifierProceedings of the ICM, Beijing 2002, vol. 3, 107--116
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/67225
dc.subjectProbability
dc.subject60F05, 60F15, 60F17
dc.titleEstimates for the strong approximation in multidimensional central limit theorem
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