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Stable convergence and stable limit theorems [electronic resource]
- 作者： Hausler, Erich.
- 其他作者：
- 其他題名：
- Probability theory and stochastic modelling,
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Probability theory and stochastic modelling,v.74
- 主題： Limit theorems (Probability theory) , Convergence , Mathematics , Probability Theory and Stochastic Processes. , Statistical Theory and Methods.
- ISBN： 9783319183299 (electronic bk.) 、 9783319183282 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 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.
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讀者標籤：
- 系統號： 005134949 | 機讀編目格式
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