Ulteriori informazioni
Informationen zum Autor Thomas W. O'gorman , PhD, is Associate Professor in the Department of Mathematical Sciences at Northern Illinois University. Dr. O'Gorman's current research focuses on the analysis of adaptive methods for performing statistical tests and confidence intervals. Klappentext Provides the tools needed to successfully perform adaptive tests across a broad range of datasetsAdaptive 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 estimatesThroughout 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. "Each chapter provides detailed information on R and SAS code, respectively. Moreover, each chapter closes with illustrating exercises (without solutions). This is ideal for researchers who wish to implement anadaptive test of significance for their specific problem." ( Biometrical Journal , 1 May 2013) Zusammenfassung 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. Inhaltsverzeichnis Preface xv 1 Introduction 1 1.1 Why Use Adaptive Tests? 1 1.2 A Brief History of Adaptive Tests 2 1.3 The Adaptive Test of Hogg, Fisher, and Randies 5 1.4 Limitations of Rank-Based Tests 8 1.5 The Adaptive Weighted Least Squares Approach 9 1.6 Development of the Adaptive WLS Test 12 2 Smoothing Methods and Normalizing Transformations 15 2.1 Traditional Estimators of the Median and the Interquartile Range 15 2.2 Percentile Estimators that Use the Smooth Cumulative Distribution Function 16 2.3 Estimating the Bandwidth 21 2.4 Normalizing Transformations 23 2.5 The Weighting Algorithm 27 2.6 Computing the Bandwidth 30 2.7 Examples of Transformed Data 37 3 A Two-Sample Adaptive Test 43 3.1 A Two-Sample Model 44 3.2 Computing the Adaptive Weights 45 3.3 The Test Statistics for Adaptive Tests 47 3.4 Permutation Methods for Two-Sample Tests 50 3.5 An Example of a Two-Sample Test 54 3.6 R Code for the Two-Sample Test 56 3.7 Level of Significance of the Adaptive Test 61 3.8 Power of the Adaptive Test 63 3.9 Sample Size Estimation 65 3.10 A SAS Macro for the Adaptive Test 68 3.11 Modifications for One-Tailed Tests 70 3.12 Justification of the We...
