Maximum Empirical Likelihood Estimation In A Heteroscedastic Linear Regression ModelWith Possibly Missing Responses

A heteroscedastic linear regression model is considered where responses are allowed to be missing at random and with the conditional variance modeled as a function of the mean response. Maximum empirical likelihood estimation is studied for an empirical likelihood with an increasing number of estimated constraints. The resulting estimator is shown to be asymptotically normal and can outperform the ordinary least squares estimator.

DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7791