Ulteriori informazioni
Informationen zum Autor George A.F. Seber and Christopher J. Wild are professors in the Department of Statistics at The University of Auckland in New Zealand. Klappentext WILEY-INTERSCIENCE PAPERBACK SERIESThe 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 Zusammenfassung 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. Inhaltsverzeichnis 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. ...
Sommario
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.
Relazione
"...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)
