Nonparametric Statistical Methods Using R (Hardcover)

A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.

General

Imprint:	Taylor & Francis
Country of origin:	United States
Series:	Chapman & Hall/CRC Texts in Statistical Science
Release date:	October 2014
First published:	2022
Authors:	John Kloke • Joseph W. McKean
Dimensions:	234 x 156 x 21mm (L x W x T)
Format:	Hardcover
Pages:	287
ISBN-13:	978-1-4398-7343-4
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
LSN:	1-4398-7343-7
Barcode:	9781439873434

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