£105.94

Springer Robust Rank-Based and Nonparametric Methods Book

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Description

Explore advanced statistical research in Robust Rank-Based and Nonparametric Methods, a comprehensive collection of selected, revised, and extended contributions from the April 2015 Michigan conference. This Springer Proceedings in Mathematics and Statistics volume brings together distinguished researchers who have shaped the field through collaboration with Joseph McKean. Readers will find deep insights into the development of underlying theory, small sample corrections, and efficient computation algorithms. The content spans a wide range of modern statistical needs, from traditional robust nonparametric rank-based procedures to cutting-edge Bayesian and big data rank-based analyses. This text is designed for professionals and academics looking to apply these methods in specialized fields such as biostatistics and spatial areas. As robust rank-based and nonparametric methods have evolved significantly over the last 30 years, this book serves as a vital resource for understanding both established principles and new developments in the discipline.

Key Features

Features contributions from distinguished researchers and scholars who have collaborated with Joseph McKean on statistical theory.

Covers a wide scope of research including Bayesian approaches and modern big data rank-based analyses.

Provides technical depth on small sample corrections and the development of efficient algorithms for computation.

Offers practical applications for specialized professional fields such as biostatistics and spatial areas.

Documents 30 years of development in robust rank-based and nonparametric statistical methods.

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Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
21 September 2016
Listed Since
13 April 2016

Barcode

No barcode data available

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