Nonparametric statistical methods using R / John Kloke, University of Wisconsin, Madison, WI, USA, Joseph W. McKean, Western Michigan University, Kalamazoo, MI, USA.
By: Kloke, John.
Contributor(s): McKean, Joseph W.
Material type:
BookSeries: Chapman & Hall/CRC the R series.Description: xv, 271 pages : illustrations ; 9x6.ISBN: 9780367739720.Subject(s): Nonparametric statistics | R (Computer program language)DDC classification: 519.54/Klo/McK Summary:
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.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Book
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Mathematics | 519.54/Klo/McK (Browse shelf) | Available | 25324 |
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"A Chapman & Hall book."
Includes bibliographical references (pages 255-263) and index.
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.
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