All of Nonparametric Statistics

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Beschreibung

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 inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master¿s-level students. For Ph. D.

Zusammenfassung

Aimed at Masters or PhD level students in statistics, computer science, and engineering, this comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. With an exhaustive exploration of asymptotic nonparametric inferences, it also covers a huge range of other crucial topic areas including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book's dual approach includes a mixture of methodology and theory.

Inhaltsverzeichnis

Estimating the CDF and Statistical Functionals.- The Bootstrap and the Jackknife.- Smoothing: General Concepts.- Nonparametric Regression.- Density Estimation.- Normal Means and Minimax Theory.- Nonparametric Inference Using Orthogonal Functions.- Wavelets and Other Adaptive Methods.- Other Topics.

Details

Erscheinungsjahr:	2005
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Reihe:	Springer Texts in Statistics
Inhalt:	xii 270 S.
ISBN-13:	9780387251455
ISBN-10:	0387251456
Sprache:	Englisch
Herstellernummer:	10945858
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Wasserman, Larry
Hersteller:	Springer US Springer New York Springer US, New York, N.Y. Springer Texts in Statistics
Maße:	241 x 160 x 21 mm
Von/Mit:	Larry Wasserman
Erscheinungsdatum:	21.10.2005
Gewicht:	0,594 kg

Artikel-ID: 102280265

Zusammenfassung

Inhaltsverzeichnis

Details

Erscheinungsjahr:	2005
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Reihe:	Springer Texts in Statistics
Inhalt:	xii 270 S.
ISBN-13:	9780387251455
ISBN-10:	0387251456
Sprache:	Englisch
Herstellernummer:	10945858
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Wasserman, Larry
Hersteller:	Springer US Springer New York Springer US, New York, N.Y. Springer Texts in Statistics
Maße:	241 x 160 x 21 mm
Von/Mit:	Larry Wasserman
Erscheinungsdatum:	21.10.2005
Gewicht:	0,594 kg