Parametric robust inference about regression parameters for the correlation coefficient

Abstract

This article establishes a robust likelihood function about regression parameters for the correlation coefficients modeled in a generalized linear model fashion. The validity of the proposed likelihood requires no knowledge of the true underlying distributions, so long as they have finite fourth moments. The efficacy of the robust methodology is shown via simulations. The asymptotic variance of the maximum-likelihood estimate and the empirical error probabilities of the resultant robust likelihood ratio test are specifically exhibited.