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A Special Purpose Linear Programming Algorithm for Obtaining Least Absolute Value Estimators in a Linear Model with Dummy Variables.

机译:一种求解虚拟变量线性模型最小绝对值估计的专用线性规划算法。

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Dummy (0, 1) variables are frequently used in statistical modeling to represent the effect of certain extraneous factors. This paper presents a special purpose linear programming algorithm for obtaining least-absolute-value estimators in a linear model with dummy variables. The algorithm employs a compact basis inverse procedure and incorporates the advanced basis exchange techniques available in specialized algorithms for the general linear least-absolute-value problem. Computational results with a computer code version of the algorithm are given. (Author)

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