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A Parametric Approach to Nonparametric Statistics. ノンパラメトリック統計学への パラメトリック・アプローチ
・ISBN 978-3-319-94152-3 hard EUR 69.99
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| 著者・編者 | Alvo, Mayer / Yu, P. L. H., |
|---|---|
| シリーズ | Springer Series in the Data Sciences |
| 出版社 | (Springer, GW) |
| 出版年月 | 2018 |
| ページ数 | 279 pp. |
| 言語 | ENG |
| ニュース番号 | ＜647-409 647-P855＞ |
解説
This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter.
This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.