Adaptive Tests of Significance Using Permutations of Residuals with R and SAS

By:

O'Gorman, Thomas W [author]

Material type:

TextLanguage: English Publication details: Hoboken : Wiley , 2012 .Description: xvii,345pISBN:

9780470922255

Subject(s):

Other classification:

B284 Q2;1 TB

Summary: Adaptive Tests of Significance Using Permutations of Residuals with R and SAS illustrates the power of adaptive tests and showcases their ability to adjust the testing method to suit a particular set of data. The book utilizes state-of-the-art software to demonstrate the practicality and benefits for data analysis in various fields of study. Beginning with an introduction, the book moves on to explore the underlying concepts of adaptive tests, including, Smoothing methods and normalizing transformations Permutation tests with linear methods Applications of adaptive tests, Multicenter and cross-over trials, Analysis of repeated measures data Adaptive confidence intervals and estimates, Throughout the book, numerous figures illustrate the key differences among traditional tests, nonparametric tests, and adaptive tests. R and SAS software packages are used to perform the discussed techniques, and the accompanying datasets are available on the book's related website. In addition, exercises at the end of most chapters enable readers to analyze the presented datasets by putting new concepts into practice.

Item type:

Textbook

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Textbook	Central Science Library	Central Science Library			B284 Q2;1 TB (Browse shelf(Opens below))					Available			SL1558583

Includes References 333-340p.; Index 341-345p.

Adaptive Tests of Significance Using Permutations of Residuals with R and SAS illustrates the power of adaptive tests and showcases their ability to adjust the testing method to suit a particular set of data. The book utilizes state-of-the-art software to demonstrate the practicality and benefits for data analysis in various fields of study.
Beginning with an introduction, the book moves on to explore the underlying concepts of adaptive tests, including, Smoothing methods and normalizing transformations Permutation tests with linear methods
Applications of adaptive tests, Multicenter and cross-over trials, Analysis of repeated measures data
Adaptive confidence intervals and estimates, Throughout the book, numerous figures illustrate the key differences among traditional tests, nonparametric tests, and adaptive tests. R and SAS software packages are used to perform the discussed techniques, and the accompanying datasets are available on the book's related website. In addition, exercises at the end of most chapters enable readers to analyze the presented datasets by putting new concepts into practice.

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