QR decomposition based orthogonality estimation for partially linear models with longitudinal data

This paper studies the estimation for a class of partially linear models with longitudinal data. By combining quadratic inference functions with QR decomposition technology, we propose a new estimation method for the parametric and nonparametric components. The resulting estimators for parametric and nonparametric components do not affect each other, and then it is easy for application in practice. Under some mild conditions, we establish some asymptotic properties of the resulting estimators. Some simulation studies are undertaken to assess the finite sample performance of the proposed estimation procedure.