Modern Applied U-Statistics

Modern Applied U-Statistics PDF

Author: Jeanne Kowalski

Publisher: John Wiley & Sons

Published: 2008-01-28

Total Pages: 402

ISBN-13: 0470186453

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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.

U-Statistics

U-Statistics PDF

Author: A J. Lee

Publisher: Routledge

Published: 2019-03-13

Total Pages: 324

ISBN-13: 1351405853

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In 1946 Paul Halmos studied unbiased estimators of minimum variance, and planted the seed from which the subject matter of the present monograph sprang. The author has undertaken to provide experts and advanced students with a review of the present status of the evolved theory of U-statistics, including applications to indicate the range and scope of U-statistic methods. Complete with over 200 end-of-chapter references, this is an invaluable addition to the libraries of applied and theoretical statisticians and mathematicians.

A Modern Introduction to Probability and Statistics

A Modern Introduction to Probability and Statistics PDF

Author: F.M. Dekking

Publisher: Springer Science & Business Media

Published: 2006-03-30

Total Pages: 488

ISBN-13: 1846281687

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Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books

All of Statistics

All of Statistics PDF

Author: Larry Wasserman

Publisher: Springer Science & Business Media

Published: 2013-12-11

Total Pages: 446

ISBN-13: 0387217363

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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Asymptotic Statistics

Asymptotic Statistics PDF

Author: A. W. van der Vaart

Publisher: Cambridge University Press

Published: 2000-06-19

Total Pages: 470

ISBN-13: 9780521784504

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This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master s level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.

Fundamentals of Modern Statistical Methods

Fundamentals of Modern Statistical Methods PDF

Author: Rand R. Wilcox

Publisher: Springer Science & Business Media

Published: 2010-03-18

Total Pages: 255

ISBN-13: 1441955240

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Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size.

Distribution-Free Statistical Methods

Distribution-Free Statistical Methods PDF

Author: J. S. Maritz

Publisher: Springer

Published: 1981

Total Pages: 282

ISBN-13:

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Basic concepts in distribution-free methods; One-sample location problems; Miscellaneous one-sample problems; Two-sample problems; Straight-line regression; Multiple regression and general linear models; Bivariate problems; Appendix; Bibliography.

Modern Interdisciplinary University Statistics Education

Modern Interdisciplinary University Statistics Education PDF

Author: National Research Council

Publisher: National Academies Press

Published: 1994-02-01

Total Pages: 150

ISBN-13: 0309050332

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This book examines how the discipline of statistics should respond to the changing environment in which statisticians work. What does the academic, industry, and government customer need? How can the content of courses and of the overall statistics educational experience be arranged to address the customer's needs? Interdisciplinary needs are described, and successful university programs in interdisciplinary statistics are detailed.