Adaptive Tests
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This book successfully introduces readers to the theory and applicability of adaptive tests, reviews the main contributions in the field, and provides readers with the tools needed to implement the statistical methodology. It focuses on adaptive tests of significance, which are more powerful than traditional tests. Also included is coverage of smoothing methods in adaptive weighting, permutation of residuals, and within-block permutation methods, allowing the presented methods to be used directly with multicenter clinical trials as well as with randomized block designs. It is designed for professionals in a variety of fields, including biostatistics and pharmaceutical, agricultural, and business statistics, as well as a supplementary course book for courses in regression analysis and adaptive analysis.
Provides the tools needed to successfully perform adaptive tests across a broad range of datasets
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.
Adaptive Tests of Significance Using Permutations of Residuals with R and SAS is an insightful reference for professionals and researchers working with statistical methods across a variety of fields including the biosciences, pharmacology, and business. The book also serves as a valuable supplement for courses on regression analysis and adaptive analysis at the upper-undergraduate and graduate levels.
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.
Adaptive Tests of Significance Using Permutations of Residuals with R and SAS is an insightful reference for professionals and researchers working with statistical methods across a variety of fields including the biosciences, pharmacology, and business. The book also serves as a valuable supplement for courses on regression analysis and adaptive analysis at the upper-undergraduate and graduate levels.
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