Empirical likelihood-based inferences in varying coefficient models with missing data

Abstract

In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = Xτα(U) + ɛ when X are subject to missing at random. Based on the inverse probability-weighted idea, a class of empirical log-likelihood ratios, as well as two maximum empirical likelihood estimators, are developed for α(u). The resulting statistics are shown to have standard chi-squared or normal distributions asymptotically. Simulation studies are also constructed to illustrate the finite sample properties of the proposed statistics.