This book provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book covers the underpinnings of appropriate statistical inference in addition to new theoretical methods, open problems, and novel testing procedures. It also offers software routines for a majority of the methods based on R and SAS.
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