IJAPM 2014 Vol.4(4): 223-226 ISSN: 2010-362X
DOI: 10.7763/IJAPM.2014.V4.287
DOI: 10.7763/IJAPM.2014.V4.287
A Unified Linear Regression Approach
Kim Fung Lam
Abstract—Despite the popularity of least-squares regression
in linear regression analysis, it is well known to be sensitive to
extreme values. Many researchers have developed a number of
alternative estimators. Among the various estimators, least
absolute deviationsisone of the most popular alternatives. Some
earlier research works attempted to combine least-squares
regression and least absolute deviations regression via
non-linear programming approaches. Instead of using
non-linear programming approaches, this paper introduces a
linear programming model combining least-squares regression
and least absolute deviations regression in linear regression
analysis. The proposed linear programming model is
computationally simpler than existing non-linear programming
approaches suggested in the literature. Another advantage of
the linear programming models is additional constraints and
different objective coefficients can be easily added in the
formulations. Moreover, the proposed linear programming
model can be employed in combining forecasts.
Index Terms—Least absolute deviations, least squares least-squares regression, linear programming.
Kim Fung Lam is with the Department of Management Sciences, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong (e-mail: msblam@cityu.edu.hk).
Index Terms—Least absolute deviations, least squares least-squares regression, linear programming.
Kim Fung Lam is with the Department of Management Sciences, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong (e-mail: msblam@cityu.edu.hk).
Cite: Kim Fung Lam, "A Unified Linear Regression Approach," International Journal of Applied Physics and Mathematics vol. 4, no. 4, pp. 223-226, 2014.
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General Information
ISSN: 2010-362X (Online)
Abbreviated Title: Int. J. Appl. Phys. Math.
Frequency: Quarterly
APC: 500USD
DOI: 10.17706/IJAPM
Editor-in-Chief: Prof. Haydar Akca
Abstracting/ Indexing: INSPEC(IET), CNKI, Google Scholar, EBSCO, Chemical Abstracts Services (CAS), etc.
E-mail: ijapm@iap.org
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