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Titlebook: High Dimensional Probability VI; The Banff Volume Christian Houdré,David M. Mason,Jon A. Wellner Conference proceedings 2013 Springer Basel

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書目名稱High Dimensional Probability VI
副標(biāo)題The Banff Volume
編輯Christian Houdré,David M. Mason,Jon A. Wellner
視頻videohttp://file.papertrans.cn/427/426220/426220.mp4
概述Gives a unique view on the mathematical methods used by experts to establish limit theorems in probability and statistics, which reside in high dimensions.Displays the wide scope of the types of probl
叢書名稱Progress in Probability
圖書封面Titlebook: High Dimensional Probability VI; The Banff Volume Christian Houdré,David M. Mason,Jon A. Wellner Conference proceedings 2013 Springer Basel
描述.This is a collection of papers by participants at High Dimensional Probability VI Meeting held from October 9-14, 2011 at the Banff International Research Station in Banff, Alberta, Canada.?.High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other areas of mathematics, statistics, and computer science. These include random matrix theory, nonparametric statistics, empirical process theory, statistical learning theory, concentration of measure phenomena, strong and weak approximations, distribution function estimation in high dimensions, combinatorial optimization, and random graph theory. .The papers in this volume?show that HDP theory continues to develop new tools, methods, techniques and perspectives to analyze the random phenomena. Both researchers and advanced students will find this book of great use for learning about new avenues of research.?.
出版日期Conference proceedings 2013
關(guān)鍵詞high dimensional probability; limit theorems; probability distributions
版次1
doihttps://doi.org/10.1007/978-3-0348-0490-5
isbn_softcover978-3-0348-0799-9
isbn_ebook978-3-0348-0490-5Series ISSN 1050-6977 Series E-ISSN 2297-0428
issn_series 1050-6977
copyrightSpringer Basel 2013
The information of publication is updating

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Maximal Inequalities for Centered Norms of Sums of Independent Random VectorsLet . be independent random variables and . We show that for any constants ...We also discuss similar inequalities for sums of Hilbert and Banach spacevalued random vectors.
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On Some Gaussian Concentration Inequality for Non-Lipschitz FunctionsA concentration inequality for functions of a pair of Gaussian random vectors is established. Instead of the usual Lipschitz condition some boundedness of second-order derivatives is assumed. This result can be viewed as an extension of a well-known tail estimate for Gaussian random bi-linear forms to the non-linear case.
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Strong Log-concavity is Preserved by Convolutionon of strong log-concavity are known in the discrete setting (where strong log-concavity is known as “ultra-log-concavity”), preservation of strong logconcavity under convolution has apparently not been investigated previously in the continuous case.
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