| 書(shū)目名稱 | Permutation, Parametric, and Bootstrap Tests of Hypotheses | | 編輯 | Phillip Good | | 視頻video | http://file.papertrans.cn/745/744230/744230.mp4 | | 概述 | Contains valuable techniques for reducing computation time,.practical advice on experimental design, comparisons with bootstrap, parametric, and nonparametric techniques and a three-part bibliography | | 叢書(shū)名稱 | Springer Series in Statistics | | 圖書(shū)封面 |  | | 描述 | This text is intended to provide a strong theoretical background in testing hypotheses and decision theory for those who will be practicing in the real worldorwhowillbeparticipatinginthetrainingofreal-worldstatisticiansand biostatisticians. In previous editions of this text, my rhetoric was somewhat tentative. I was saying, in e?ect, “Gee guys, permutation methods provide a practical real-world alternative to asymptotic parametric approximations. Why not give them a try?” But today, the theory, the software, and the hardware have come together. Distribution-free permutation procedures are the primary method for testing hypotheses. Parametric procedures and the bootstrap are to be reserved for the few situations in which they may be applicable. Four factors have forced this change: 1. Desire by workers in applied ?elds to use the most powerful statistic for their applications. Such workers may not be aware of the fundamental lemma of Neyman and Pearson, but they know that the statistic they wanttouse—acomplexscoreoraratioofscores,doesnothaveanalready well-tabulated distribution. 2. Pressure from regulatory agencies for the use of methods that yield exact signi?cance levels, not appr | | 出版日期 | Book 2005Latest edition | | 關(guān)鍵詞 | calculus; clustering; efficiency; permutation tests | | 版次 | 3 | | doi | https://doi.org/10.1007/b138696 | | isbn_softcover | 978-1-4419-1907-6 | | isbn_ebook | 978-0-387-27158-3Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer-Verlag New York 2005 |
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