Mathematical Algorithms for Linear Regression (Computer Science and Scientific Computing)

This volume presents an overview of numerical methods for linear regression, including FORTRAN subroutines. Linear regression has useful applications in business, statistics and engineering and this work covers all three important cases where p=1,2 and infinity. Mathematical Algorithms for Linear Regression discusses numerous fitting principles related to discrete linear approximations, corresponding numerical methods, and FORTRAN 77 subroutines. The book explains linear Lp regression, method of the lease squares, the Gaussian elimination method, the modified Gram-Schmidt method, the method of least absolute deviations, and the method of least maximum absolute deviation. The investigator can determine which observations can be classified as outliers (those with large errors) and which are not by using the fitting principle. The text describes the elimination of outliers and the selection of variables if too many or all of them are given by values. The clusterwise linear regression accounts if only a few of the relevant variables have been collected or are collectible, assuming that their number is small in relation to the number of observations. The book also examines linear Lp regression with nonnegative parameters, the Kuhn-Tucker conditions, the Householder transformations, and the branch-and-bound method. The text points out the method of least squares is mainly used for models with nonlinear parameters or for orthogonal distances. The book can serve and benefit mathematicians, students, and professor of calculus, statistics, or advanced mathematics. Content: Linear L regression robust regression (ROBUST) ridge regression (RRL2, RRL1, RRL1) linear L regression with linear constraints linear L regression with non-negative parameters orthogonal linear L regression final remarks list of subroutines examples. Helmuth Späth. Translation Of: Mathematische Software Zur Linearen Regression. Includes Bibliographical References And Index.