All of Nonparametric Statistics

2006
ISBN: 978-0-387-25145-5
Verlag: Springer

Buch, Englisch, 270 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1270 g

Reihe: Springer Texts in Statistics

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.

Zielgruppe

Research

Autoren/Hrsg.

Wasserman, Larry

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

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.