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High Dimensional Probability: 43 ISBN 13: 9783034897907

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What is high dimensional probability? Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that can be immediately understood, that of Gaussian processes. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. Assuming some regularity on the covariance, one tried to take advantage of the structure of the index set. Around 1970 it was understood, in particular by Dudley, Feldman, Gross, and Segal that a more abstract and intrinsic point of view was much more fruitful. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. This shift in perspective subsequently lead to a considerable clarification of many aspects of Gaussian process theory, and also to its applications in other settings.

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Contenuti

Weak Convergence of the Row Sums of a Triangular Array of Empirical Processes.- Self-Normalized Large Deviations in Vector Spaces.- Consistency of M-Estimators and One-Sided Bracketing.- Small Deviation Probabilities of Sums of Independent Random Variables.- Strong Approximations to the Local Empirical Process.- On Random Measure Processes with Application to Smoothed Empirical Processes.- A Consequence for Random Polynomials of a Result of De La Peña and Montgomery-Smith.- Distinctions Between the Regular and Empirical Central Limit Theorems for Exchangeable Random Variables.- Laws of Large Numbers and Continuity of Processes.- Convergence in Law of Random Elements and Random Sets.- Asymptotics of Spectral Projections of Some Random Matrices Approximating Integral Operators.- A Short Proof of the Gaussian Isoperimetric Inequality.- Some Shift Inequalities for Gaussian Measures.- A Central Limit Theorem for the Sock-Sorting Problem.- Oscillations of Gaussian Stein’s Elements.- A Sufficient Condition for the Continuity of High Order Gaussian Chaos Processes.- On Wald’s Equation and First Exit Times for Randomly Stopped Processes with Independent Increments.- The Best Doob-type Bounds for the Maximum of Brownian Paths.- Optimal Tail Comparison Based on Comparison of Moments.- The Bootstrap of Empirical Processes for ?-Mixing Sequences.

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9783764358679: High Dimensional Probability: 43

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. What is high dimensional probability? Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that. Codice articolo 4319599

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -What is high dimensional probability Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that can be immediately understood, that of Gaussian processes. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. Assuming some regularity on the covariance, one tried to take advantage of the structure of the index set. Around 1970 it was understood, in particular by Dudley, Feldman, Gross, and Segal that a more abstract and intrinsic point of view was much more fruitful. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. This shift in perspective subsequently lead to a considerable clarification of many aspects of Gaussian process theory, and also to its applications in other settings. 348 pp. Englisch. Codice articolo 9783034897907

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - What is high dimensional probability Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that can be immediately understood, that of Gaussian processes. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. Assuming some regularity on the covariance, one tried to take advantage of the structure of the index set. Around 1970 it was understood, in particular by Dudley, Feldman, Gross, and Segal that a more abstract and intrinsic point of view was much more fruitful. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. This shift in perspective subsequently lead to a considerable clarification of many aspects of Gaussian process theory, and also to its applications in other settings. Codice articolo 9783034897907

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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -What is high dimensional probability Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that can be immediately understood, that of Gaussian processes. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. Assuming some regularity on the covariance, one tried to take advantage of the structure of the index set. Around 1970 it was understood, in particular by Dudley, Feldman, Gross, and Segal that a more abstract and intrinsic point of view was much more fruitful. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. This shift in perspective subsequently lead to a considerable clarification of many aspects of Gaussian process theory, and also to its applications in other settings.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 348 pp. Englisch. Codice articolo 9783034897907

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Condizione: New. Editor(s): Eberlein, Ernst; Hahn, Marjorie. Series: Progress in Probability. Num Pages: 343 pages, 2 black & white illustrations. BIC Classification: PBT. Category: (G) General (US: Trade). Dimension: 235 x 155 x 18. Weight in Grams: 534. . 1998. Softcover reprint of the original 1st ed. 1998. Paperback. . . . . Codice articolo V9783034897907

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Paperback. Condizione: new. Paperback. What is high dimensional probability? Under this broad name we collect topics with a common philosophy, where the idea of high dimension plays a key role, either in the problem or in the methods by which it is approached. Let us give a specific example that can be immediately understood, that of Gaussian processes. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. Assuming some regularity on the covariance, one tried to take advantage of the structure of the index set. Around 1970 it was understood, in particular by Dudley, Feldman, Gross, and Segal that a more abstract and intrinsic point of view was much more fruitful. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. This shift in perspective subsequently lead to a considerable clarification of many aspects of Gaussian process theory, and also to its applications in other settings. Roughly speaking, before 1970, the Gaussian processes that were studied were indexed by a subset of Euclidean space, mostly with dimension at most three. The index set was no longer considered as a subset of Euclidean space, but simply as a metric space with the metric canonically induced by the process. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9783034897907

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