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.
Reviews / Votes
"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)
