A special purpose linear programming algorithm for obtaining least absolute value estimators in a linear model with dummy variables
作者:
Ronald D. Armstrong,
Edward Frome,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1977)
卷期:
Volume 6,
issue 4
页码: 383-398
ISSN:0361-0918
年代: 1977
DOI:10.1080/03610917708812052
出版商: Marcel Dekker, Inc.
关键词: L1norm;analysis of covariance;regression;generalized upper bounding
数据来源: Taylor
摘要:
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.
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