COVID-19 Update: We are currently shipping orders daily. However, due to transit disruptions in some geographies, deliveries may be delayed. To provide all customers with timely access to content, we are offering 50% off Science and Technology Print & eBook bundle options. Terms & conditions.

# Mathematical Algorithms for Linear Regression

## 1st Edition

0.0 star rating Write a review
Author:
Editor:
eBook ISBN: 9781483264547
Published Date: 17th December 1991
Page Count: 338
Sales tax will be calculated at check-out Price includes VAT/GST
72.95
62.00
43.99
54.95
Unavailable
Price includes VAT/GST

## Description

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.

Preface

Notation

I Introduction

II Linear Lp Regession

2.1 Fundamentals

2.2 ρ = 2 (Method of the Least Squares: NGL, MGS, ICMGS, GIVR, HFTI, SVDR)

2.3 ρ ≠ 1,2, ∞ (LPREGR)

2.4 ρ = 1 (Method of Least Absolute Deviations: A478L1, AFKL1, BLOD1)

2.5 ρ = ∞ (Method of Least Maximum Absolute Deviation: A328LI, A495LI, ABDLI)

2.6 Comparison of Residuals (RES) and Choice of p

2.7 The Elimination of Outliers

2.8 Selection of Variables (SCR, SCRFL1)

2.9 Clusterwise Linear Regression (CWLL2R, CWLL1R, CWLLIR)

2.10 Average Linear Regression (AVLLSQ)

III Robust Regression (ROBUST) 193

IV Ridge Regression (RRL2, RRL1, RRL1)

V Linear Lp Regression with Linear Constraints

5.1 Introduction

5.2 p = 2(CL2)

5.3 p = 1 (CL1)

5.4 ρ = ∞ (CLI)

VI Linear Lp Regression with Nonnegative Parameters (p = 2: NNLS; p = 1: NNL1; p = ∞: NNL1)

VII Orthogonal Linear Lp Regression

7.1 Fundamentals

7.2 p = 2(L2ORTH)

7.3 p ≠ 1,2, ∞ (LPORTH)

7.4 p = 1 (L1ORTH)

7.5 p = ∞ (L1ORTH)

7.6 Comparison of Residuals and Choice of p

7.7 Orthogonal L2 Regression with Linear Manifolds (LMORTH)

Final Remarks

List of Subroutines

Appendix: Examples

Index

## Details

No. of pages:
338
Language:
English
Published:
17th December 1991
Imprint: