Orthogonality based modal empirical likelihood inferences for partially nonlinear models

This paper explored the effective empirical likelihood inferences for partially nonlinear models. By combining the modal regression method with orthogonal projection technology, a modal empirical likelihood-based estimation procedure was proposed. The proposed empirical likelihood approach retained Wilk's theorem under mild conditions, and the confidence regions of model coefficients were constructed. Nonparametric and parametric components of the estimators were independent. Simulation results demonstrated that it is more robust and effective than the existing methods.