本の詳細
形式
ハードカバー
ページ数
517
言語
英語
公開されました
Sep 12, 1996
出版社
Springer
説明
Jun Shao presents a comprehensive exploration of two fundamental resampling techniques in statistics: the jackknife and bootstrap methods. Through detailed explanations and practical examples, the work guides readers on how these methodologies can be applied to enhance statistical inference and improve the robustness of estimations.
The author delves into the theoretical underpinnings of these techniques while offering insights into their practical implementation. The book is accessible to a wide audience, making complex statistical concepts understandable, which will appeal to both students and practitioners in the field.
Furthermore, Shao emphasizes the importance of these methods in various statistical analyses, showing how they can lead to more reliable and flexible results. With its clear structure and engaging style, the book serves as an essential resource for anyone looking to deepen their understanding of statistical resampling methods.
The author delves into the theoretical underpinnings of these techniques while offering insights into their practical implementation. The book is accessible to a wide audience, making complex statistical concepts understandable, which will appeal to both students and practitioners in the field.
Furthermore, Shao emphasizes the importance of these methods in various statistical analyses, showing how they can lead to more reliable and flexible results. With its clear structure and engaging style, the book serves as an essential resource for anyone looking to deepen their understanding of statistical resampling methods.