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Description

All of Nonparametric Statistics from the Springer Texts in Statistics series offers a comprehensive resource for advanced learners. Designed for Masters or PhD level students in statistics, computer science, and engineering, this book provides clear accounts of up-to-date issues in nonparametric inference. It serves as an excellent tool for researchers needing to get up to speed on modern nonparametric methods through exceptional clarity. The text provides an exhaustive exploration of asymptotic nonparametric inferences. It covers a wide range of important topic areas, including the bootstrap, the nonparametric delta method, nonparametric regression, and density estimation. This single volume acts as a complete guide for those studying or working within these specialized mathematical fields.

Key Features

This Springer textbook is designed for Masters and PhD level students in statistics, computer science, and engineering.

The text provides clear accounts of up-to-date issues in nonparametric inference for advanced academic study.

Researchers can use this book to quickly learn modern nonparametric methods through its clear presentation.

The content includes an exhaustive exploration of asymptotic nonparametric inferences.

Topic areas covered include the bootstrap, the nonparametric delta method, and nonparametric regression.

This volume provides detailed information on density estimation and other essential statistical methods.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
19 November 2010
Listed Since
14 June 2010

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