| 书目名称 | The Jackknife and Bootstrap | | 编辑 | Jun Shao,Dongsheng Tu | | 视频video | | | 丛书名称 | Springer Series in Statistics | | 图书封面 |  | | 描述 | The jackknife and bootstrap are the most popular data-resampling meth ods used in statistical analysis. The resampling methods replace theoreti cal derivations required in applying traditional methods (such as substitu tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample pr | | 出版日期 | Book 1995 | | 关键词 | Bootstrapping; Covariance matrix; Estimator; Factor analysis; Generalized linear model; Likelihood; Monte | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4612-0795-5 | | isbn_softcover | 978-1-4612-6903-8 | | isbn_ebook | 978-1-4612-0795-5Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer Science+Business Media New York 1995 |
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发表于 2025-3-21 19:42:18
