A linked list data structure for a simple linear regression algorithm
type: Item
instance of: scholarly article
title: A linked list data structure for a simple linear regression algorithm
zbMATH Open document ID: 0606.90034
DOI: 10.1016/0305-0548(84)90018-2
author: Ronald D. Armstrong
author: Mabel Tam Kung
published in: Computers \& Operations Research
publication date: 1984-01-01T00:00:00Z
review text: The simple linear regression problem arises from a fundamental model of statistical and economic forecastings. The model consists of an independent (also known as predictor) random variable which is used to determine the value of the dependent (or response) random variable. The problem is to find the linear equation which will fit the data comprising these two variables. The least absolute values estimation is the criterion used here to estimate the values of these two variables by minimizing the sum of the absolute deviations of a given set of observations from the values predicted by the model. This paper presents a special purpose algorithm utilizing linear programming and a special data structure to solve this problem efficiently.
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 90C90
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C05
zbMATH DE Number: 3980863
zbMATH Keywords: simple linear regression problem
zbMATH Keywords: special data structure
describes a project that uses: AS 132
describes a project that uses: AS 74
MaRDI profile type: Publication
full work available at URL: 0305-0548(84)90018-2
OpenAlex ID: W2066817785
cites work: Algorithm AS 132: Least Absolute Value Estimates for a Simple Linear Regression Problem
cites work: An Improved Algorithm for Discrete $l_1 $ Linear Approximation
cites work: Optimality and Degeneracy in Linear Programming
cites work: Optimal estimation of executive compensation by linear programming
description: scientific article; zbMATH DE number 3980863