Nonlinear Regression Analysis and Its Applications

Bates, Douglas M.,Watts, Donald G.

ISBN 10: 0470139005 / ISBN 13: 9780470139004

Edité par Wiley-Interscience, 2007

A propos de cet article

Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! N° de réf. du vendeur S_415550410

Détails bibliographiques

Titre : Nonlinear Regression Analysis and Its ...

Éditeur : Wiley-Interscience

Date d'édition : 2007

Reliure : paperback

Etat : Good

A propos de ce titre

Synopsis :

Unusual book

Quatrième de couverture:

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.