Modern Applied U-Statistics (eBook)

A timely and applied approach to the newly discovered methods and
applications of U-statistics

Built on years of collaborative research and academic experience,
Modern Applied U-Statistics successfully presents a thorough
introduction to the theory of U-statistics using in-depth examples
and applications that address contemporary areas of study including
biomedical and psychosocial research. Utilizing a "learn by
example" approach, this book provides an accessible, yet in-depth,
treatment of U-statistics, as well as addresses key concepts in
asymptotic theory by integrating translational and
cross-disciplinary research.

The authors begin with an introduction of the essential and
theoretical foundations of U-statistics such as the notion of
convergence in probability and distribution, basic convergence
results, stochastic Os, inference theory, generalized estimating
equations, as well as the definition and asymptotic properties of
U-statistics. With an emphasis on nonparametric applications when
and where applicable, the authors then build upon this established
foundation in order to equip readers with the knowledge needed to
understand the modern-day extensions of U-statistics that are
explored in subsequent chapters. Additional topical coverage
includes:

Longitudinal data modeling with missing data

Parametric and distribution-free mixed-effect and structural
equation models

A new multi-response based regression framework for non-parametric
statistics 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) for
dependent responses

Motivating examples, in-depth illustrations of statistical and
model-building concepts, and an extensive discussion of
longitudinal study designs strengthen the real-world utility and
comprehension of this book. An accompanying Web site features SAS?
and S-Plus? program codes, software applications, and additional
study data. Modern Applied U-Statistics accommodates second- and
third-year students of biostatistics at the graduate level and also
serves as an excellent self-study for practitioners in the fields
of bioinformatics and psychosocial research.