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Essential Statistical Inference : Theory and Methods. 統計的推測要説－理論と方法
・ISBN 978-1-4614-4817-4 hard EUR 169.99
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| 著者・編者 | Boos, Dennis D. / Stefanski, L. A., |
|---|---|
| シリーズ | Springer Texts in Statistics |
| 出版社 | (Springer, US) |
| 出版年月 | 2013 |
| ページ数 | xiv, 578 pp. |
| 言語 | ENG |
| ニュース番号 | ＜598-347＞ |
解説
?This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems.
An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology.
Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. ?