Maximum likelihood estimation of linear models for longitudinal data with inequality constraints

Abstract

Maximum likelihood (ML) estimation for linear models with longitudinal data under inequality restrictions is investigated. Within-subject correlations are modeled by parametric structure. Asymptotic properties of constrained ML estimates, including strong consistency, approximate representation and asymptotic distribution, are derived. Finally, the ML estimators with and without constraints are compared in terms of sample bias, sample mean-square error MSE and sample variance of the estimators by a simulation