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NONPARAMETRIC STATISTICAL METHODS USING R
Nonparametric Statistical Methods Using R covers traditional nonparamet
ric 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 ap-plications 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 con-cepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computa-tion 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.
Features
Explains how to apply and compute nonparametric methods, such as Wilcoxon procedures and bootstrap methods
Describes various types of rank-based estimates, including linear, nonlinear, time series, and basic mixed effects models
Illustrates the use of diagnostic procedures, including studentized residuals and difference in fits
Provides the R packages on CRAN, enabling you to reproduce all of the analyses
Includes exercises at the end of each chapter for self-study and classroom use
ric 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 ap-plications 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 con-cepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computa-tion 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.
Features
Explains how to apply and compute nonparametric methods, such as Wilcoxon procedures and bootstrap methods
Describes various types of rank-based estimates, including linear, nonlinear, time series, and basic mixed effects models
Illustrates the use of diagnostic procedures, including studentized residuals and difference in fits
Provides the R packages on CRAN, enabling you to reproduce all of the analyses
Includes exercises at the end of each chapter for self-study and classroom use
Availability
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Information Technology
005.1/KLO/IT0582
IT0582
Available - Available
Detail Information
- Series Title
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- Call Number
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005.1/KLO
- Publisher
- New York : CRC Press., 2015
- Collation
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- Language
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- ISBN/ISSN
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978-1-4398-7343-4
- Classification
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005.1/KLO
- Content Type
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- Media Type
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- Carrier Type
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- Edition
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First Edition
- Subject(s)
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- Specific Detail Info
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- Statement of Responsibility
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