Buch, Englisch, 798 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 1191 g
Buch, Englisch, 798 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 1191 g
Reihe: Wiley Series in Probability and Statistics
ISBN: 978-0-471-47135-6
Verlag: Wiley
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.
From the Reviews of Nonlinear Regression
"A very good book and an important one in that it is likely to become a standard reference for all interested in nonlinear regression; and I would imagine that any statistician concerned with nonlinear regression would want a copy on his shelves."
–The Statistician
"Nonlinear Regression also includes a reference list of over 700 entries. The compilation of this material and cross-referencing of it is one of the most valuable aspects of the book. Nonlinear Regression can provide the researcher unfamiliar with a particular specialty area of nonlinear regression an introduction to that area of nonlinear regression and access to the appropriate references. Nonlinear Regression provides by far the broadest discussion of nonlinear regression models currently available and will be a valuable addition to the library of anyone interested in understanding and using such models including the statistical researcher."
–Mathematical Reviews
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
