A　kernel　regression　imputation　method　for　missing　response　data　is　developed.　A　class　of　bias-corrected　empirical　log-likelihood　ratios　for　the　response　mean　is　defined.　It　is　shown　that　any　member　of　our　class　of　ratios　is　asymptotically　chi-squared,　and　the　corresponding　empirical　likelihood　confidence　interval　for　the　response　mean　is　constructed.　Our　ratios　share　some　of　the　desired　features　of　the　existing　methods:　they　are　self-scale　invariant　and　no　plug-in　estimators　for　the　adjustment　factor　and　asymptotic　variance　are　needed;　when　estimating　the　non-parametric　function　in　the　model,　undersmoothing　to　ensure　root-n　consistency　of　the　estimator　for　the　parameter　is　avoided.　Since　the　range　of　bandwidths　contains　the　optimal　bandwidth　for　estimating　the　regression　function,　the　existing　data-driven　algorithm　is　valid　for　selecting　an　optimal　bandwidth.　We　also　study　the　normal　approximation-based　method.　A　simulation　study　is　undertaken　to　compare　the　empirical　likelihood　with　the　normal　approximation　method　in　terms　of　coverage　accuracies　and　average　lengths　of　confidence　intervals.