دانلود مقاله : Estimation and empirical likelihood for single index models with missing data in th

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