Comparing Least-Squares and Goal Programming Estimates of Linear Regression Parameters

被引:0
作者
Ahmad, Maizah Hura [1 ]
Adnan, Robiah [1 ]
Kong, Lau Chik [1 ]
Daud, Zalina Mohd [2 ]
机构
[1] Univ Teknol Malaysia, Fac Sci, Dept Math, Skudai 4130, Malaysia
[2] ATMA, Kuala Lumpur, Malaysia
关键词
Method of least squares; outliers; goal programming;
D O I
暂无
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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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页码:101 / 112
页数:12
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