  We consider an empirical likelihood inference for parameters defined by
general estimating equations when some components of the random observations
are subject to missingness. As the nature of the estimating equations is
wide-ranging, we propose a nonparametric imputation of the missing values from
a kernel estimator of the conditional distribution of the missing variable
given the always observable variable. The empirical likelihood is used to
construct a profile likelihood for the parameter of interest. We demonstrate
that the proposed nonparametric imputation can remove the selection bias in the
missingness and the empirical likelihood leads to more efficient parameter
estimation. The proposed method is further evaluated by simulation and an
empirical study on a genetic dataset on recombinant inbred mice.
