Items related to Generalized Inference in Repeated Measures: Exact Methods...
Synopsis
This book presents some of the most recent developments and classical methods in Multivariate Analysis of Variance (MANOVA), Repeated Measures, and Growth Curves. It attempts to deal with the problem of poor approximations by offering more exact methods for data analysis. Through examples such as following the change in consumer demand of a product over time and analyzing data from clinical trials, Exact Methods in MANOVA, Mixed Models, and Repeated Measures shows readers how these methods are more useful and more accurate in predicting results.
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About the Author
SAMARADASA WEERAHANDI, PhD, is Director of Statistics at TDS, Time Warner. The author of more than fifty papers and contributor to three edited volumes, he is a Fellow of the American Statistical Association. He received his PhD from the University of British Columbia.
From the Back Cover
A complete guide to powerful and practical statistical modeling using MANOVA
Numerous statistical applications are time dependent. Virtually all biomedical, pharmaceutical, and industrial experiments demand repeated measurements over time. The same holds true for market research and analysis. Yet conventional methods, such as the Repeated Measures Analysis of Variance (Rm ANOVA), do not always yield exact solutions, obliging practitioners to settle for asymptotic results and approximate solutions. Generalized inference in Multivariate Analysis of Variance (MANOVA), mixed models, and growth curves offer exact methods of data analysis under milder conditions without deviating from the conventional philosophy of statistical inference.
Generalized Inference in Repeated Measures is a concise, self-contained guide to the use of these innovative solutions, presenting them as extensions of rather than alternatives to classical methods of statistical evaluation. Requiring minimal prior knowledge of statistical concepts in the evaluation of linear models, the book provides exact parametric methods for each application considered, with solutions presented in terms of generalized p-values. Coverage includes:
- New concepts in statistical inference, with special focus on generalized p-values and generalized confidence intervals
- One-way and two-way ANOVA, in cases of equal and unequal variances
- Basic and higher-way mixed models, including testing and estimation of fixed effects and variance components
- Multivariate populations, including basic inference, comparison, and analysis of variance
- Basic, widely used repeated measures models including crossover designs and growth curves
With a comprehensive set of formulas, illustrative examples, and exercises in each chapter, Generalized Inference in Repeated Measures is ideal as both a comprehensive reference for research professionals and a text for students.
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