| 書目名稱 | Permutation Methods |
| 副標(biāo)題 | A Distance Function |
| 編輯 | Paul W. Mielke,Kenneth J. Berry |
| 視頻video | http://file.papertrans.cn/745/744221/744221.mp4 |
| 概述 | Makes a variety of powerful data analytic tools easily available to practitioners.Includes supplementary material: |
| 叢書名稱 | Springer Series in Statistics |
| 圖書封面 |  |
| 描述 | The introduction of permutation tests by R. A. Fisher relaxed the paramet- ric structure requirement of a test statistic. For example, the structure of the test statistic is no longer required if the assumption of normality is removed. The between-object distance function of classical test statis- tics based on the assumption of normality is squared Euclidean distance. Because squared Euclidean distance is not a metric (i. e. , the triangle in- equality is not satisfied), it is not at all surprising that classical tests are severely affected by an extreme measurement of a single object. A major purpose of this book is to take advantage of the relaxation of the struc- ture of a statistic allowed by permutation tests. While a variety of distance functions are valid for permutation tests, a natural choice possessing many desirable properties is ordinary (i. e. , non-squared) Euclidean distance. Sim- ulation studies show that permutation tests based on ordinary Euclidean distance are exceedingly robust in detecting location shifts of heavy-tailed distributions. These tests depend on a metric distance function and are reasonably powerful for a broad spectrum of univariate and multivaria |
| 出版日期 | Book 20011st edition |
| 關(guān)鍵詞 | Permutation Methods; permutation tests; regression analysis; robust statistics; statistical inference; st |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4757-3449-2 |
| isbn_ebook | 978-1-4757-3449-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer Science+Business Media New York 2001 |
|Archiver|手機版|小黑屋|
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