The canonical way to establish the central limit theorem for i.i.d. random variables is to use characteristic functions and L???vy's continuity theorem. This monograph focuses on this characteristic function approach and presents a renormalization theory called mod- convergence. This type of convergence is a relatively new concept with many deep ramifications, and has not previously been published in a single accessible volume. The authors construct an extremely flexible framework using this concept in order to study limit ...
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The canonical way to establish the central limit theorem for i.i.d. random variables is to use characteristic functions and L???vy's continuity theorem. This monograph focuses on this characteristic function approach and presents a renormalization theory called mod- convergence. This type of convergence is a relatively new concept with many deep ramifications, and has not previously been published in a single accessible volume. The authors construct an extremely flexible framework using this concept in order to study limit theorems and large deviations for a number of probabilistic models related to classical probability, combinatorics, non-commutative random variables, as well as geometric and number-theoretical objects. Intended for researchers in probability theory, the text is carefully well-written and well-structured, containing a great amount of detail and interesting examples.
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Add this copy of Mod- Convergence: Normality Zones and Precise to cart. $51.65, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2016 by Springer.
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New. Print on demand Trade paperback (US). Glued binding. 152 p. Contains: Unspecified, Illustrations, black & white, Illustrations, color, Tables, color. Springerbriefs in Probability and Mathematical Statistics.
