Efficient parameter estimation in regression with missing responses

Abstract

We discuss efficient estimation in regression models that are defined by a finite-dimensional parametric constraint. This includes a variety of regression models, in particular the basic nonlinear regression model and quasi-likelihood regression. We are interested in the case where responses are missing at random. This is a popular research topic and various methods have been proposed in the literature. However, many of them are complicated and are not shown to be efficient. The method presented here is, in contrast, very simple – we use an estimating equation that does not impute missing responses – and we also prove that it is efficient if an appropriate weight matrix is selected. Finally, we show that this weight matrix can be replaced by a consistent estimator without losing the efficiency property.