Empirical-Likelihood-Based Inferences for Generalized Partially Linear Models.

This paper considers generalized partially linear models. We propose empirical likelihood based statistics to construct confidence regions for the parametric and nonparametric componenets. The resulting statistics are shown to be asymptotically chi-squared distributed. Finite sample performance of the proposed statistics is assessed by simulation experiments. The proposed methods are applied to a dataset from an AIDS clinical trial.