Choosing the best set of variables in regression analysis using integer programming
Choosing the best set of variables in regression analysis using integer programming
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DOI：
10.1007/s10898-008-9323-9
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发表时间：
2009-06-01
影响因子：
1.8
通讯作者：
Yamamoto, Rei
中科院分区：
文献类型：
--
作者：
Konno, Hiroshi;Yamamoto, Rei
本文关注的是一种在多元线性回归模型中从 k(> s) 个候选变量中选择最佳 s 个变量集的算法。我们采用绝对偏差作为偏差的度量，并使用 0-1 整数规划方法解决由此产生的优化问题。此外，我们将提出一种启发式算法来获得一组接近最佳平方偏差的变量。计算结果表明该方法对于确定最佳变量集实用且可靠。
This paper is concerned with an algorithm for selecting the best set of s variables out of k(> s) candidate variables in a multiple linear regression model. We employ absolute deviation as the measure of deviation and solve the resulting optimization problem by using 0-1 integer programming methodologies. In addition, we will propose a heuristic algorithm to obtain a close to optimal set of variables in terms of squared deviation. Computational results show that this method is practical and reliable for determining the best set of variables.