Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12077
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dc.contributor.authorRajab, RS-
dc.contributor.authorDražić, M-
dc.contributor.authorMladenović, N-
dc.contributor.authorMladenović, P-
dc.contributor.authorYu, K-
dc.date.accessioned2016-02-11T11:10:03Z-
dc.date.available2015-11-01-
dc.date.available2016-02-11T11:10:03Z-
dc.date.issued2015-
dc.identifier.citationJournal of Global Optimization, 63(3): pp. 481 - 500, (2015)en_US
dc.identifier.issn0925-5001-
dc.identifier.issn1573-2916-
dc.identifier.urihttp://link.springer.com/article/10.1007%2Fs10898-015-0311-6-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12077-
dc.description.abstractQuantile regression is an increasingly important topic in statistical analysis. However, fitting censored quantile regression is hard to solve numerically because the objective function to be minimized is not convex nor concave in regressors. Performance of standard methods is not satisfactory, particularly if a high degree of censoring is present. The usual approach is to simplify (linearize) estimator function, and to show theoretically that such approximation converges to optimal values. In this paper, we suggest a new approach, to solve optimization problem (nonlinear, nonconvex, and nondifferentiable) directly. Our method is based on variable neighborhood search approach, a recent successful technique for solving global optimization problems. The presented results indicate that our method can improve quality of censored quantizing regressors estimator considerably.en_US
dc.format.extent481 - 500-
dc.language.isoenen_US
dc.publisherSpringer USen_US
dc.subjectCensored regressionen_US
dc.subjectPowell estimatoren_US
dc.subjectQuantile regressionen_US
dc.subjectGlobal optimizationen_US
dc.subjectMetaheuristicsen_US
dc.subjectVariable neighborhood searchen_US
dc.titleFitting censored quantile regression by variable neighborhood searchen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s10898-015-0311-6-
dc.relation.isPartOfJournal of Global Optimization-
pubs.issue3-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.volume63-
Appears in Collections:Dept of Mathematics Research Papers

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