Existing　methods　for　generating　common　weights　in　data　envelopment　analysis　(DEA)　are　either　very　complicated　or　unable　to　produce　a　full　ranking　for　decision　making　units　(DMUs).　This　paper　proposes　a　new　methodology　based　on　regression　analysis　to　seek　a　common　set　of　weights　that　are　easy　to　estimate　and　can　produce　a　full　ranking　for　DMUs.　The　DEA　efficiencies　obtained　with　the　most　favorable　weights　to　each　DMU　are　treated　as　the　target　efficiencies　of　DMUs　and　are　best　fitted　with　the　efficiencies　determined　by　common　weights.　Two　new　nonlinear　regression　models　are　constructed　to　optimally　estimate　the　common　weights.　Four　numerical　examples　are　examined　using　the　developed　new　models　to　test　their　discrimination　power　and　illustrate　their　potential　applications　in　fully　ranking　DMUs.　Comparisons　with　a　similar　compromise　approach　for　generating　common　weights　are　also　discussed.　©　2011　Published　by　Elsevier　Ltd.