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Titlebook: All of Nonparametric Statistics; Larry Wasserman Book 2006 Springer-Verlag New York 2006 Excel.Parametric statistics.STATISTICA.WholePage.

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期刊全稱All of Nonparametric Statistics
影響因子2023Larry Wasserman
視頻videohttp://file.papertrans.cn/154/153419/153419.mp4
發(fā)行地址There are many books on various aspects of nonparametric inference but no other book covers all the topics in one place.Offers a brief account of the modern topics in nonparametric inference.Includes
學科分類Springer Texts in Statistics
圖書封面Titlebook: All of Nonparametric Statistics;  Larry Wasserman Book 2006 Springer-Verlag New York 2006 Excel.Parametric statistics.STATISTICA.WholePage.
影響因子There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inferenc
Pindex Book 2006
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Nonparametric Inference Using Orthogonal Functions,sity estimation into a Normal means problem and then we construct estimates and confidence sets using the theory from Chapter 7. In the regression case, the resulting estimators are linear smoothers and thus are a special case of the estimators described in Section 5.2. We discuss another approach t
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978-1-4419-2044-7Springer-Verlag New York 2006
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