A　partial　linear　model　with　missing　response　variables　and　error-prone　covariates　is　considered.　The　imputation　approach　is　developed　to　estimate　the　regression　coefficients　and　the　nonparametric　function.　The　proposed　parametric　estimators　are　shown　to　be　asymptotically　normal,　and　the　estimators　for　the　nonparametric　part　are　proved　to　converge　at　an　optimal　rate.　To　construct　confidence　regions　for　the　regression　coefficients　and　the　nonparametric　function,　respectively,　the　authors　also　propose　the　empirical-likelihood-based　statistics　and　investigate　the　limit　distributions　of　the　empirical　likelihood　ratios.　The　simulation　study　is　conducted　to　compare　the　finite　sample　behavior　for　the　proposed　estimators.　An　application　to　an　AIDS　dataset　is　illustrated.