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A Parametric Approach to Nonparametric Statistics / by Mayer Alvo, Philip L. H. Yu
(Springer Series in the Data Sciences. ISSN:23655682)
版 | 1st ed. 2018. |
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出版者 | Cham : Springer International Publishing : Imprint: Springer |
出版年 | 2018 |
大きさ | XIV, 279 p. 15 illus. in color : online resource |
著者標目 | *Alvo, Mayer author Yu, Philip L. H author SpringerLink (Online service) |
件 名 | LCSH:Probabilities LCSH:Statistics FREE:Probability Theory FREE:Statistical Theory and Methods |
一般注記 | I. Introduction and Fundamentals -- Introduction -- Fundamental Concepts in Parametric Inference -- II. Modern Nonparametric Statistical Methods -- Smooth Goodness of Fit Tests -- One-Sample and Two-Sample Problems -- Multi-Sample Problems -- Tests for Trend and Association -- Optimal Rank Tests -- Efficiency -- III. Selected Applications -- Multiple Change-Point Problems -- Bayesian Models for Ranking Data -- Analysis of Censored Data -- A. Description of Data Sets 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 HTTP:URL=https://doi.org/10.1007/978-3-319-94153-0 |
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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9783319941530 |
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EB00200252 |
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データ種別 | 電子ブック |
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分 類 | LCC:QA273.A1-274.9 DC23:519.2 |
書誌ID | 4000115919 |
ISBN | 9783319941530 |
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※2017年9月4日以降