Least Squares Data Fitting with Applications

As one of the classical statistical regression techniques, and often the first to be taught to new students, least squares fitting can be a very effective tool in data analysis. Given measured data, we establish a relationship between independent and dependent variables so that we can use the data predictively. The main concern of "Least Squares Data Fitting with Applications" is how to do this on a computer with efficient and robust computational methods for linear and nonlinear relationships. The presentation also establishes a link between the statistical setting and the computational issues. In a number of applications, the accuracy and efficiency of the least squares fit is central, and Per Christian Hansen, Victor Pereyra, and Godela Scherer survey modern computational methods and illustrate them in fields ranging from engineering and environmental sciences to geophysics. Anyone working with problems of linear and nonlinear least squares fitting will find this book invaluable as a hands-on guide, with accessible text and carefully explained problems. Included are: an overview of computational methods together with their properties and advantages; topics from statistical regression analysis that help readers to understand and evaluate the computed solutions; and many examples that illustrate the techniques and algorithms. "Least Squares Data Fitting with Applications" can be used as a textbook for advanced undergraduate or graduate courses and professionals in the sciences and in engineering.

Table of Contents:
Preface Symbols and Acronyms Chapter 1. The Linear Data Fitting Problem 1.1. Parameter estimation, data approximation 1.2. Formulation of the data fitting problem 1.3. Maximum likelihood estimation 1.4. The residuals and their properties 1.5. Robust regression Chapter 2. The Linear Least Squares Problem 2.1. Linear least squares problem formulation 2.2. The QR factorization and its role 2.3. Permuted QR factorization Chapter 3. Analysis of Least Squares Problems 3.1. The pseudoinverse 3.2. The singular value decomposition 3.3. Generalized singular value decomposition 3.4. Condition number and column scaling 3.5. Perturbation analysis Chapter 4. Direct Methods for Full-Rank Problems 4.1. Normal equations 4.2. LU factorization 4.3. QR factorization 4.4. Modifying least squares problems 4.5. Iterative refinement 4.6. Stability and condition number estimation 4.7. Comparison of the methods Chapter 5. Direct Methods for Rank-Deficient Problems 5.1. Numerical rank 5.2. Peters-Wilkinson LU factorization 5.3. QR factorization with column permutations 5.4. UTV and VSV decompositions 5.5. Bidiagonalization 5.6. SVD computations Chapter 6. Methods for Large-Scale Problems 6.1. Iterative versus direct methods 6.2. Classical stationary methods 6.3. Non-stationary methods, Krylov methods 6.4. Practicalities: preconditioning and stopping criteria 6.5. Block methods Chapter 7. Additional Topics in Least Squares 7.1. Constrained linear least squares problems 7.2. Missing data problems 7.3. Total least squares (TLS) 7.4. Convex optimization 7.5. Compressed sensing Chapter 8. Nonlinear Least Squares Problems 8.1. Introduction 8.2. Unconstrained problems 8.3. Optimality conditions for constrained problems 8.4. Separable nonlinear least squares problems 8.5. Multiobjective optimization Chapter 9. Algorithms for Solving Nonlinear LSQ Problems 9.1. Newton's method 9.2. The Gauss-Newton method 9.3. The Levenberg-Marquardt method 9.4. Additional considerations and software 9.5. Iteratively reweighted LSQ algorithms for robust data fitting problems 9.6. Variable projection algorithm 9.7. Block methods for large-scale problems Chapter 10. Ill-Conditioned Problems 10.1. Characterization 10.2. Regularization methods 10.3. Parameter selection techniques 10.4. Extensions of Tikhonov regularization 10.5. Ill-conditioned NLLSQ problems Chapter 11. Linear Least Squares Applications 11.1. Splines in approximation 11.2. Global temperatures data fitting 11.3. Geological surface modeling Chapter 12. Nonlinear Least Squares Applications 12.1. Neural networks training 12.2. Response surfaces, surrogates or proxies 12.3. Optimal design of a supersonic aircraft 12.4. NMR spectroscopy 12.5. Piezoelectric crystal identification 12.6. Travel time inversion of seismic data Appendix A: Sensitivity Analysis A.1. Floating-point arithmetic A.2. Stability, conditioning and accuracy Appendix B: Linear Algebra Background B.1. Norms B.2. Condition number B.3. Orthogonality B.4. Some additional matrix properties Appendix C: Advanced Calculus Background C.1. Convergence rates C.2. Multivariable calculus Appendix D: Statistics D.1. Definitions D.2. Hypothesis testing References Index

About the Author :
Per Christian Hansen is a professor of scientific computing at the Technical University of Denmark. Victor Pereyra is a consulting professor of energy resources engineering at Stanford University and was a principal at Weidlinger Associates, Los Altos, California. Godela Scherer is a visiting research fellow in the Department of Mathematics at the University of Reading, United Kingdom, and a professor of scientific computing at the Universidad Simon Bolivar, Venezuela.

Review :
Least Square Data fitting with Applications is a book that will be of great practical use to anyone whose work involves the analysis of data and its modeling. It offers a great deal of information that can be a s valuable in the lecture theater as in the lab or office. Mathematics Today