Stable convergence and stable limit ...
Hausler, Erich.

 

  • Stable convergence and stable limit theorems[electronic resource] /
  • 紀錄類型: 書目-語言資料,印刷品 : Monograph/item
    杜威分類號: 519.2
    書名/作者: Stable convergence and stable limit theorems/ by Erich Hausler, Harald Luschgy.
    作者: Hausler, Erich.
    其他作者: Luschgy, Harald.
    出版者: Cham : : Springer International Publishing :, 2015.
    面頁冊數: x, 228 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    標題: Limit theorems (Probability theory)
    標題: Convergence.
    標題: Mathematics.
    標題: Probability Theory and Stochastic Processes.
    標題: Statistical Theory and Methods.
    ISBN: 9783319183299 (electronic bk.)
    ISBN: 9783319183282 (paper)
    內容註: Preface -- 1.Weak Convergence of Markov Kernels -- 2.Stable Convergence -- 3.Applications -- 4.Stability of Limit Theorems -- 5.Stable Martingale Central Limit Theorems -- 6.Stable Functional Martingale Central Limit Theorems -- 7.A Stable Limit Theorem with Exponential Rate -- 8.Autoregression of Order One -- 9.Branching Processes -- A. Appendix -- B. Appendix -- Bibliography.
    摘要、提要註: 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 Renyi, 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.
    電子資源: http://dx.doi.org/10.1007/978-3-319-18329-9
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