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Comparing least-squares and goal programming estimates of linear regression parameter.

Ahmad, Maizah Hura and Adnan, Robiah and Lau, Chik Kong and Mohd. Daud, Zalina (2005) Comparing least-squares and goal programming estimates of linear regression parameter. Matematika, 21 (2). pp. 101-112. ISSN 0127-8274

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Abstract

A regression model is a mathematical equation that describes the relationship between two or more variables. In regression analysis, the basic idea is to use past data to fit a prediction equation that relates a dependent variable to independent variable(s). This prediction equation is then used to estimate future values of the dependent variable. The least-squares method is the most frequently used procedure for estimating the regression model parameters. However, the method of least-squares is biased when outliers exist. This paper proposes goal programming as a method to estimate regression model parameters when outliers must be included in the analysis.

Item Type:Article
Uncontrolled Keywords:method of least squares, outliers, goal programming
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:8795
Deposited By: Ms Zalinda Shuratman
Deposited On:12 May 2009 07:01
Last Modified:02 Jun 2010 01:57

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