Empirical Processes Based upon Residuals from Errors-in-Variables Regressions

Abstract

Multivariate errors-in-variables regression models with normal errors are considered and residuals, similar to those calculated from ordinary least squares regressions, are defined for these models. It is shown that under the assumption of a $n^{1/2}$-consistent estimator of the vector of regression coefficients, certain empirical processes based upon the residuals converge to the same Gaussian process as that of an infinite sequence of normal random variables standardized by their sample mean sample variance.