| 书目名称 | Resampling Methods for Dependent Data |
| 编辑 | S. N. Lahiri |
| 视频video | |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | This is a book on bootstrap and related resampling methods for temporal and spatial data exhibiting various forms of dependence. Like the resam pling methods for independent data, these methods provide tools for sta tistical analysis of dependent data without requiring stringent structural assumptions. This is an important aspect of the resampling methods in the dependent case, as the problem of model misspecification is more preva lent under dependence and traditional statistical methods are often very sensitive to deviations from model assumptions. Following the tremendous success of Efron‘s (1979) bootstrap to provide answers to many complex problems involving independent data and following Singh‘s (1981) example on the inadequacy of the method under dependence, there have been several attempts in the literature to extend the bootstrap method to the dependent case. A breakthrough was achieved when resampling of single observations was replaced with block resampling, an idea that was put forward by Hall (1985), Carlstein (1986), Kiinsch (1989), Liu and Singh (1992), and others in various forms and in different inference problems. There has been a vig orous development in the |
| 出版日期 | Book 2003 |
| 关键词 | Bootstrapping; Resampling; Ringe; STATISTICA; permutation tests; statistics |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4757-3803-2 |
| isbn_softcover | 978-1-4419-1848-2 |
| isbn_ebook | 978-1-4757-3803-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2003 |
发表于 2025-3-21 16:07:32
