Robust inference for the correlation coefficient - A parametric method

This article introduces a parametric robust way of making valid inferences for the correlation coefficient. More specifically, it will be demonstrated that the bivariate normal likelihood function can be made asymptotically valid for practically all underlying bivariate continuous distributions. The adjustment to the bivariate normal model that achieves the robustness property will be presented. Simulation studies will be performed to demonstrate the finite sample performance of the novel robust procedure.