  This paper addresses estimation in a longitudinal regression model for
association between a scalar outcome and a set of longitudinally-collected
functional covariates or predictor curves. The framework consists of estimating
a time-varying coefficient function that is modeled as a linear combination of
time-invariant functions but having time-varying coefficients. The estimation
procedure exploits the equivalence between penalized least squares estimation
and a linear mixed model representation. The process is empirically evaluated
with several simulations and it is applied to analyze the neurocognitive
impairment of HIV patients and its association with longitudinally-collected
magnetic resonance spectroscopy curves.
