Subset Selection by Mallows' Cp: A Mixed Integer Programming Approach
Ryuhei Miyashiro (r-miyacc.tuat.ac.jp)
Abstract: This paper concerns a method of selecting the best subset of explanatory variables for a linear regression model. Employing Mallows' C_p as a goodness-of-fit measure, we formulate the subset selection problem as a mixed integer quadratic programming problem. Computational results demonstrate that our method provides the best subset of variables in a few seconds when the number of candidate explanatory variables is less than 30. Furthermore, when handling datasets consisting of a large number of samples, it finds better-quality solutions faster than stepwise regression methods do.
Keywords: Subset selection, Mixed integer programming, Mallows' C_p, Linear regression model
Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )
Category 2: Nonlinear Optimization (Quadratic Programming )
Category 3: Applications -- Science and Engineering (Statistics )
Citation: Published in Expert Systems with Applications, 42 (2015), 325-331.
Entry Submitted: 01/18/2014
Modify/Update this entry
|Visitors||Authors||More about us||Links|
Search, Browse the Repository
Give us feedback
|Optimization Journals, Sites, Societies|