Description
Book SynopsisProvides a survey of aspects of model building and statistical inference. This book presents a synthesis of theoretical literature, requiring only familiarity with linear regression methods. It contains three chapters on central computational questions that comprise a self contained introduction to unconstrained optimization.
Trade Review"…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)
Table of Contents1. 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.
