Abstract We present a robust regression estimator for longitudinal data, which is especially suited for functional data that has been observed on sparse or irregular time grids. We show by simulation that the proposed estimators possess good outlier-resistance properties com- pared with the traditional functional least-squares estimator. As an example of application, we study the relationship between levels of oxides of nitrogen and ozone in the city of San Francisco. Key Words: Functional data analysis; Longitudinal data analysis; Mixed effects mod- els; Robust statistics; Spline smoothing.