Maximum likelihood computations for regression with measurement error

This paper presents a general computational method for maximum likelihood analysis for generalized regression with measurement error in a single explanatory variable. The method is the EM algorithm with Gauss-Hermite quadrature in the E-step. Although computationally intensive, this method provides maximum likelihood estimation under a broad range of distributional assumptions. This is important because maximum likelihood estimators can be more efficient than commonly used moment estimators and likelihood ratio tests and confidence intervals can be substantially superior to those based on asymptotic normality with approximate standard errors. (C) 2001 Elsevier Science B.V. All rights reserved.