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Linear Programming Techniques in Regression Analysis
Author(s) -
Kiountouzis E. A.
Publication year - 1973
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2346304
Subject(s) - linear regression , linear programming , computer science , proper linear model , regression analysis , statistics , bayesian multivariate linear regression , mathematics , machine learning , algorithm
Summary In this paper simulation techniques are used to evaluate the use of linear programming in regression analysis. The experiments demonstrate that, in certain cases, minimizing the sum of the absolute values of the deviations ( L 1 norm) is preferable to the Least Squares criterion. No significant bias was found in the L 1 norm estimates.