Contents Preface v Chapter 1. Introduction and historic overview 1 1.1. Classical regression 1 1.2. Errors-in-variables (EIV) model 3 1.3. Geometric fit 5 1.4. Solving a general EIV problem 8 1.5. Non-linear nature of the 'linear' EIV 11 1.6. Statistical properties of the orthogonal fit 13 1.7. Relation to total least squares (TLS) 15 1.8. Nonlinear models 16 1.9. Notation and preliminaries 17 ...

Geometric iterative methods, including progressive approximation and geometric interpolation are efficient for fitting a given data set. With the development of big technology, number points has become massive, least-squares (LSPIA) is generally applied to fit mass data. Combining Schulz method calculating Moore–Penrose generalized inverse matrix with traditional LSPIA method, this paper presen...

This contribution gives a brief overview on nonlinear constrained least squares methods. The focus is on data fitting problems in which the models include ordinary or partial differential equations.

Today's overall grasp and prediction of the trend changes in sports scores, led this study to carry out the discussion that the athletes’ obtaining reasonable suggestion is particularly important to fundamentally improve the comprehensive quality of track and field athletes performance. The Olympic Games is the big games, and the ‘stand or fall’ of achievements is the only measure of players pe...

Methods are proposed for estimating real lifetimes and corresponding coefficients from realvalued measurement data in logarithmic scale, where the data are multi-exponential, i.e. represented by linear combinations of decaying exponential functions with various lifetimes. Initial approximations of lifetimes are obtained as peaks of the first derivative of the data, where the first derivative ca...