Comparing least-squares and goal programming estimates of linear regression parameter.
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Department of Mathematics, Faculty of Science
2005
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| Subjects: | |
| Online Access: | http://eprints.utm.my/8795/ http://eprints.utm.my/8795/1/MaizahHuraAhmad2005_ComparingLeast-SquaresandGoalProgramming.pdf |
| Summary: | 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. |
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