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A sharp concentration inequality with applications

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Abstract

We present a new general concentration-of-measure inequality and illustrate its power by applications in random combinatorics. The results find direct applications in some problems of learning theory.

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  • Stéphane Boucheron & Gábor Lugosi & Pascal Massart, 1999. "A sharp concentration inequality with applications," Economics Working Papers 376, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:376
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    File URL: https://econ-papers.upf.edu/papers/376.pdf
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    1. Gábor Lugosi & Andrew B. Nobel, 1998. "Adaptive model selection using empirical complexities," Economics Working Papers 323, Department of Economics and Business, Universitat Pompeu Fabra.
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    Cited by:

    1. Lee, Sungchul & Su, Zhonggen, 2002. "The symmetry in the martingale inequality," Statistics & Probability Letters, Elsevier, vol. 56(1), pages 83-91, January.
    2. Olivier Bousquet, 2003. "New approaches to statistical learning theory," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 371-389, June.

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    More about this item

    Keywords

    Concentration of measure; Vapnik-Chervonenkis dimension; logarithmic Sobolev inequalities; longest monotone subsequence; model selection;
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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