چکیده :
The estimation and empirical likelihood for single-index models with missing covariates
are studied. A generalized estimating equations estimator for index coefficients with
missing covariates is constructed, and its asymptotic distribution is obtained. The local
linear estimator for link function achieves optimal convergence rate. By using the biascorrection
and inverse selection probability weighted methods, a class of empirical
likelihood ratios is proposed such that each of our class of ratios is asymptotically chisquared.
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 illustrated