Mathematical Algorithms for Linear Regression
eBook - PDF

Mathematical Algorithms for Linear Regression

  1. 338 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Mathematical Algorithms for Linear Regression

About this book

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.

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Yes, you can access Mathematical Algorithms for Linear Regression by Helmuth Späth, Werner Rheinboldt in PDF and/or ePUB format, as well as other popular books in Mathematics & Applied Mathematics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2014
Print ISBN
9780126564600
eBook ISBN
9781483264547
Topic
Mathematics
Subtopic
Applied Mathematics
Index
Mathematics

Table of contents

  1. Front Cover
  2. Mathematical Algorithms for Linear Regression
  3. Copyright Page
  4. Table of Contents
  5. Preface
  6. Notation
  7. Chapter I. Introduction
  8. Chapter II. Linear Lp Regression
  9. Chapter III. Robust Regression (ROBUST)
  10. Chapter IV. Ridge Regression (RRL2, RRL1, RRLl)
  11. Chapter V. Linear Lp Regression with Linear Constraints
  12. Chapter VI. Linear Lp Regression with Nonnegative Parameters (p = 2: NNLS; p = 1: NNL1 ; p = ∞: NNLI)
  13. Chapter VII. Orthogonal Linear Lp Regression
  14. Final Remarks
  15. List of Subroutines
  16. Appendix: Examples
  17. Index