In　this　paper,　we　investigate　model　selection　and　model　averaging　based　on　rank　regression.　Under　mild　conditions,　we　propose　a　focused　information　criterion　and　a　frequentist　model　averaging　estimator　for　the　focused　parameters　in　rank　regression　model.　Compared　to　the　least　squares　method,　the　new　method　is　not　only　highly　efficient　but　also　robust.　The　large　sample　properties　of　the　proposed　procedure　are　established.　The　finite　sample　properties　are　investigated　via　extensive　Monte　Claro　simulation　study.　Finally,　we　use　the　Boston　Housing　Price　Dataset　to　illustrate　the　use　of　the　proposed　rank　methods.