Abstract
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.
Original language | English |
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Pages (from-to) | 273-282 |
Number of pages | 10 |
Journal | Journal of Global Optimization |
Volume | 44 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2009 Jun |
Externally published | Yes |
Keywords
- 0-1 integer programming
- Cardinality constraint
- Least absolute deviation
- Linear regression
- Variable selection
ASJC Scopus subject areas
- Computer Science Applications
- Management Science and Operations Research
- Control and Optimization
- Applied Mathematics