| 书目名称 | Stochastic Models for Time Series |
| 编辑 | Paul Doukhan |
| 视频video | |
| 概述 | Offers mathematically oriented statisticians tools for studying non-linear time-series.Discusses moment based techniques.Richly illustrated with examples and simulations.Provides material for mathemat |
| 丛书名称 | Mathématiques et Applications |
| 图书封面 |  |
| 描述 | .This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as w |
| 出版日期 | Textbook 2018 |
| 关键词 | 60G10 37M10 32A25 60F05 60F15 60G18; 60G12 60J05 62J12 62M10 62M15 91B84; Non-linear time se |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-76938-7 |
| isbn_softcover | 978-3-319-76937-0 |
| isbn_ebook | 978-3-319-76938-7Series ISSN 1154-483X Series E-ISSN 2198-3275 |
| issn_series | 1154-483X |
| copyright | Springer International Publishing AG, part of Springer Nature 2018 |
发表于 2025-3-21 16:47:52
