The　paper　uses　the　empirical　likelihood　method　to　study　the　construction　of　confidence　intervals　and　regions　for　regression　coefficients　and　response　mean　in　generalized　linear　models　with　missing　response.　By　using　the　inverse　selection　probability　weighted　imputation　technique,　the　proposed　empirical　likelihood　ratios　are　asymptotically　chi-squared.　Our　approach　is　to　directly　calibrate　the　empirical　likelihood　ratio,　which　is　called　as　a　bias-correction　method.　Also,　a　class　of　estimators　for　the　parameters　of　interest　is　constructed,　and　the　asymptotic　distributions　of　the　proposed　estimators　are　obtained.　A　simulation　study　indicates　that　the　proposed　methods　are　comparable　in　terms　of　coverage　probabilities　and　average　lengths/areas　of　confidence　intervals/regions.　An　example　of　a　real　data　set　is　used　for　illustrating　our　methods.　(C)　2011　Elsevier　B.V.　All　rights　reserved.