A　bias-corrected　technique　for　constructing　the　empirical　likelihood　ratio　is　used　to　study　a　semiparametric　regression　model　with　missing　response　data.　We　are　interested　in　inference　for　the　regression　coefficients,　the　baseline　function　and　the　response　mean.　A　class　of　empirical　likelihood　ratio　functions　for　the　parameters　of　interest　is　defined　so　that　undersmoothing　for　estimating　the　baseline　function　is　avoided.　The　existing　data-driven　algorithm　is　also　valid　for　selecting　an　optimal　bandwidth.　Our　approach　is　to　directly　calibrate　the　empirical　log-likelihood　ratio　so　that　the　resulting　ratio　is　asymptotically　chi-squared.　Also,　a　class　of　estimators　for　the　parameters　of　interest　is　constructed,　their　asymptotic　distributions　are　obtained,　and　consistent　estimators　of　asymptotic　bias　and　variance　are　provided.　Our　results　can　be　used　to　construct　confidence　intervals　and　bands　for　the　parameters　of　interest.　A　simulation　study　is　undertaken　to　compare　the　empirical　likelihood　with　the　normal　approximation-based　method　in　terms　of　coverage　accuracies　and　average　lengths　of　confidence　intervals.　An　example　for　an　AIDS　clinical　trial　data　set　is　used　for　illustrating　our　methods.　(C)　2010　Elsevier　Inc.　All　rights　reserved.