
Stable Convergence and Stable Limit Theorems by Harald Luschgy
The authors present a concise but complete exposition of the mathematical theory of stable convergence and give various applications in different areas of probability theory and mathematical statistics to illustrate the usefulness of this concept. Stable convergence holds in many limit theorems of probability theory and statistics – such as the classical central limit theorem – which are usually formulated in terms of convergence in distribution. Originated by Alfred Rényi, the notion of stable convergence is stronger than the classical weak convergence of probability measures. A variety of methods is described which can be used to establish this stronger stable convergence in many limit theorems which were originally formulated only in terms of weak convergence. Naturally, these stronger limit theorems have new and stronger consequences which should not be missed by neglecting the notion of stable convergence. The presentation will be accessible to researchers and advanced students at the master's level with a solid knowledge of measure theoretic probability.-
Foundations of Modern Probability
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Probabilistic Theory of Mean Field Games with Applications I
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Geometry of Level Sets of Random Fields, Kac–Rice Formulas, Hermite Expansions and Applications
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Dynamics on Graphs
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Hybrid Switching Diffusions
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Nonlinear Expectations and Stochastic Calculus under Uncertainty
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Probability on Compact Lie Groups
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Measure-Valued Branching Markov Processes
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Pseudo-Regularly Varying Functions and Generalized Renewal Processes
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Stochastic Disorder Problems
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Dirichlet Forms Methods for Poisson Point Measures and Lévy Processes
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Analysis and Approximation of Rare Events
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Random Ordinary Differential Equations and Their Numerical Solution
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Stochastic Evolution Systems
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Ambit Stochastics
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Mathematical Control Theory for Stochastic Partial Differential Equations
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Dirichlet Forms Methods for Poisson Point Measures and Levy Processes
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Lectures on Monte Carlo Theory
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Stochastic Control Theory
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Random Measures, Theory and Applications
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Yosida Approximations of Stochastic Differential Equations in Infinite Dimensions and Applications
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Random Walks in the Quarter Plane
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Stochastic Flows and Jump-Diffusions
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Stochastic Multi-Stage Optimization
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The Quasispecies Equation and Classical Population Models
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Asymptotic Theory of Weakly Dependent Random Processes
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Stochastic Integration in Banach Spaces
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Continuous-Time Markov Decision Processes
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Dynamic Markov Bridges and Market Microstructure
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Limit Theorems for Multi-Indexed Sums of Random Variables
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Markov Renewal and Piecewise Deterministic Processes
“This book presents an account of stable convergence and stable limit theorems which can serve as an introduction to the area… The book is a big account of all major stable limit theorems which have been established in the last 50 years or so.” (Nikolai N. Leonenko, zbMATH 1356.60004, 2017)
“The present book represents a comprehensive account of the theory of stable convergence. The theory is illustrated by a number of examples and applied to a variety of limit theorems. … The book is well written, and the concepts are clearly explained. I enjoyed reading it because of both the contents and the authors’ attractive style of presentation. … I concur with this and think that the book will appeal to the student as much as to the specialist.” (Alexander Iksanov, Mathematical Reviews, February, 2016)
| SKU | Unavailable |
| ISBN 13 | 9783319365190 |
| ISBN 10 | 3319365193 |
| Title | Stable Convergence and Stable Limit Theorems |
| Author | Harald Luschgy |
| Series | Probability Theory And Stochastic Modelling |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2016-10-15 |
| Number of pages | 228 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
































