Nonlinear regression analysis and its applications

Author: Wei Bocheng
Publisher:
Publish Date: 1997-11-01
Features: This book briefly reviews the linear least squares method and its geometric meaning to help readers better understand linear and nonlinear least squares. Chapter 2 discusses how to establish nonlinear models and explains how to apply linear regression methods for iteration to estimate unknown parameters. It introduces how to use linear regression methods to approximate inferences about parameters and nonlinear model functions, and emphasizes the application of geometric methods once again. Chapter 3 provides a detailed discussion of practical considerations in nonlinear regression, including the selection of initial values, parameter transformations, derivative-free methods, handling of correlated residuals and cumulative data, and model comparison. Chapter 4 discusses special methods for processing multivariate response data. Chapter 5 examines partial models where the response function is a solution to a linear differential equation system, along with techniques for handling it. Chapter 6 discusses improved methods for describing nonlinear analytical inference, including the use of trace plots and section t-plots. Finally, Chapter 7 introduces measures of nonlinear intensity for specific models and datasets.