Modern Applied U–Statistics
John Wiley & Sons Inc (Hersteller)
978-0-470-18646-6 (ISBN)
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The purpose of this book is to introduce the theory of U Statistics and illustrate it with a wide range of timely applications arising in genetics, biomedical and psychological research. The most distinguishing feature includes the integration of the class U Statistics theory with modern robust inference theory, such as the generalized estimating equations, to define a new inference paradigm that is a hybrid between these fields.
Jeanne Kowalski, PhD, is Assistant Professor in the Division of Oncology Biostatistics at The Johns Hopkins University. Dr. Kowalski has authored or coauthored over thirty journal articles that focus on a wide range of issues in medicine and public health through the use of novel statistical methods, including U-statistics, generalized linear mixed-effects models, generalized estimating equations, asymptotics, and measurement error models. Xin M. Tu, PhD, is Professor in the Department of Biostatistics and Computational Biology as well as the Department of Psychiatry at The University of Rochester in New York. Dr. Tu has authored or coauthored over ninety publications in peer-reviewed journals during his career and is acclaimed as one of the best-versed authorities in the area of U-statistics.
| Erscheint lt. Verlag | 1.1.2008 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Gewicht | 10 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| ISBN-10 | 0-470-18646-1 / 0470186461 |
| ISBN-13 | 978-0-470-18646-6 / 9780470186466 |
| Zustand | Neuware |
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