  A nonlinear model with response variable missing at random is studied. In
order to improve the coverage accuracy, the empirical likelihood ratio (EL)
method is considered. The asymptotic distribution of EL statistic and also of
its approximation is $\chi^2$ if the parameters are estimated using least
squares(LS) or least absolute deviation(LAD) method on complete data. When the
response are reconstituted using a semiparametric method, the empirical
log-likelihood associated on imputed data is also asymptotically $\chi^2$. The
Wilk's theorem for EL for parameter on response variable is also satisfied. It
is shown via Monte Carlo simulations that the EL methods outperform the normal
approximation based method in terms of coverage probability up to and including
on the reconstituted data. The advantages of the proposed method are
exemplified on the real data.
