Duality in robust linear regression using Huber's M-estimator
| dc.citation.epage | 70 | en_US |
| dc.citation.issueNumber | 4 | en_US |
| dc.citation.spage | 65 | en_US |
| dc.citation.volumeNumber | 10 | en_US |
| dc.contributor.author | Pınar, M. Ç. | en_US |
| dc.date.accessioned | 2015-07-28T11:56:09Z | |
| dc.date.available | 2015-07-28T11:56:09Z | |
| dc.date.issued | 1997-07 | en_US |
| dc.department | Department of Industrial Engineering | en_US |
| dc.description.abstract | The robust linear regression problem using Huber's piecewise-quadratic M-estimator function is considered. Without exception, computational algorithms for this problem have been primal in nature. In this note, a dual formulation of this problem is derived using Lagrangean duality. It is shown that the dual problem is a strictly convex separable quadratic minimization problem with linear equality and box constraints. Furthermore, the primal solution (Huber's M-estimate) is obtained as the optimal values of the Lagrange multipliers associated with the dual problem. As a result, Huber's M-estimate can be computed using off-the-shelf optimization software. | en_US |
| dc.identifier.doi | 10.1016/S0893-9659(97)00061-X | en_US |
| dc.identifier.issn | 0893-9659 | |
| dc.identifier.uri | http://hdl.handle.net/11693/10871 | |
| dc.language.iso | English | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1016/S0893-9659(97)00061-X | en_US |
| dc.source.title | Applied Mathematics Letters | en_US |
| dc.subject | Lagrangean duality | en_US |
| dc.subject | Huber's M-estimator | en_US |
| dc.subject | Robust regression | en_US |
| dc.subject | Quadratic programming | en_US |
| dc.title | Duality in robust linear regression using Huber's M-estimator | en_US |
| dc.type | Article | en_US |
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