Asymptotic Properties of Least-Squares Estimates in Stochastic Regression Models

Abstract

Strong consistency of least-squares estimates in stochastic regression models is established under the assumption that the underlying model can be reparametrized so that the new design vectors are weakly correlated. An application to fixed-width interval estimation in stochastic approximation schemes is also discussed.