Nonlinear Regression
Published on 9. September 2003
Book
Paperback/Softback
768 pages
978-0-471-47135-6 (ISBN)
Description
This text/reference provides a broad survey of aspects of model-building and statistical inference. Presents an accessible synthesis of current theoretical literature, requiring only familiarity with linear regression methods. The three chapters on central computational questions comprise a self-contained introduction to unconstrained optimization. Includes many illustrative practical examples.
Reviews / Votes
"...a classic well written book that attempts to understand statistical ideas and computing tools in building nonlinear regression." (Journal of Statistical Computation and Simulation, July 2005)"I hope that Wiley's release of this book will rekindle some interest in this important and inappropriately overlooked subject." (International Society of Clinical Biostatistics, December 2005)
"...should be present in any statistical library." (Biometrical Journal, 2006)
More details
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 42 mm
Weight
1191 gr
ISBN-13
978-0-471-47135-6 (9780471471356)
Schweitzer Classification
Other editions
Additional editions
George A. F. Seber | C. J. Wild
Nonlinear Regression
E-Book
02/2005
Wiley
€148.99
Available for download
G. A. F. Seber | C. J. Wild
Nonlinear Regression
Book
03/1989
Wiley
€143.61
Article exhausted; check different version
Persons
George A.F. Seber and Christopher J. Wild are professors in the Department of Statistics at The University of Auckland in New Zealand.
Content
1. Model Building.
2. Estimation Methods.
3. Commonly Encountered Problems.
4. Measures of Curvature and Nonlinearity.
5. Statistical Inference.
6. Autocorrelated Errors.
7. Growth Models.
8. Compartmental Models.
9. Multiphase and Spline Regressions.
10. Errors-In-Variables Models.
11. Multiresponse Nonlinear Models.
12. Asymptotic Theory.
13. Unconstrained Optimization.
14. Computational Methods for Nonlinear Least Squares.
15. Software Considerations.
Appendix A. Vectors and Matrices
Appendix B. Differential Geometry.
Appendix C. Stochastic Differential Equations.
Appendix D. Multiple Linear Regression.
Appendix E. Minimization Subject to Linear Constraints.
References.
Author Index.
Subject Index.