Nonlinear Regression Analysis and Its Applications

Wiley-Interscience Paperback Series

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

The authors have put together an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models . . . highly recommend ed] . . . for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models.
--Technometrics

This book] provides a good balance of relevant theory and application with many examples . . . and it] provides the most balanced approach to theory and application appropriate for a first course in nonlinear regression modeling for graduate statistics students.
--Mathematical Reviews

This book] joins a distinguished list of publications with a reputation for balancing technical rigor with readability, and theory with application. It] upholds tradition . . . and is] a worthwhile reference for the marketing researcher with a serious interest in linear models.
--Journal of Marketing Research

This book offers a balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression and provides background material on linear regression, including the geometrical development for linear and nonlinear least squares. The authors employ real data sets throughout, and their extensive use of geometric constructs and continuing examples makes the progression of ideas appear very natural. The book also includes pseudocode for computing algorithms.

Author: Douglas M. Bates, Donald G. Watts
Publisher: Wiley-Interscience
Published: 03/01/2007
Pages: 392
Binding Type: Paperback
Weight: 1.21lbs
Size: 9.21h x 6.14w x 0.81d
ISBN: 9780470139004

About the Author

Douglas M. Bates, PhD, is Professor of Statistics at the University of Wisconsin-Madison. Donald G. Watts, PhD, is Professor Emeritus in Statistics at Queens University in Ontario, Canada.

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