Nonlinear Regression
Wiley Series in Probability and Statistics
1. Auflage September 2003
792 Seiten, Softcover
Wiley & Sons Ltd
Kurzbeschreibung
For the most part, data is not orderly or linear. While in the past, assumptions were made to convert nonlinear data into a more orderly format, today's software can analyze nonlinear data. This paperback reprint of a Wiley bestseller shows the fundamental principles behind the analyze of nonlinear data.
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.
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.
