Nonlinear Regression

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.

George A.F. Seber and Christopher J. Wild are professors in the Department of Statistics at The University of Auckland in New Zealand.

Model Building
Estimation Methods
Commonly Encountered Problems
Measures of Curvature and Nonlinearity
Statistical Inference
Autocorrelated Errors
Growth Models
Compartmental Models
Multiphase and Spline Regressions
Errors-in-Variables Models
Multiresponse Nonlinear Models
Asymptotic Theory
Unconstrained Optimization
Computational Methods for Nonlinear Least Squares
Software Considerations
Vectors
Differential Geometry
Stochastic Differential Equations
Multiple Linear Regression
Minimization Subject to Linear Constraints
References
Author Index
Subject Index
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Summary

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Table of Contents

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