Modern Applied U-Statistics

A timely and applied approach to the newly discovered methods andapplications of U-statistics Built on years of collaborative research and academic experience,Modern Applied U-Statistics successfully presents a thoroughintroduction to the theory of U-statistics using in-depth examplesand applications that address contemporary areas of study includingbiomedical and psychosocial research. Utilizing a "learn byexample" approach, this book provides an accessible, yet in-depth,treatment of U-statistics, as well as addresses key concepts inasymptotic theory by integrating translational andcross-disciplinary research. The authors begin with an introduction of the essential andtheoretical foundations of U-statistics such as the notion ofconvergence in probability and distribution, basic convergenceresults, stochastic Os, inference theory, generalized estimatingequations, as well as the definition and asymptotic properties ofU-statistics. With an emphasis on nonparametric applications whenand where applicable, the authors then build upon this establishedfoundation in order to equip readers with the knowledge needed tounderstand the modern-day extensions of U-statistics that areexplored in subsequent chapters. Additional topical coverageincludes: Longitudinal data modeling with missing data Parametric and distribution-free mixed-effect and structuralequation models A new multi-response based regression framework for non-parametricstatistics such as the product moment correlation, Kendall's tau,and Mann-Whitney-Wilcoxon rank tests A new class of U-statistic-based estimating equations (UBEE) fordependent responses Motivating examples, in-depth illustrations of statistical andmodel-building concepts, and an extensive discussion oflongitudinal study designs strengthen the real-world utility andcomprehension of this book. An accompanying Web site features SAS?and S-Plus? program codes, software applications, and additionalstudy data. Modern Applied U-Statistics accommodates second- andthird-year students of biostatistics at the graduate level and alsoserves as an excellent self-study for practitioners in the fieldsof bioinformatics and psychosocial research.