  In this paper we estimate the dynamic parameters of a time-varying
coefficient model through radial kernel functions in the context of a
longitudinal study. Our proposal is based on a linear combination of weighted
kernel functions involving a bandwidth, centered around a given set of time
points. In addition, we study different alternatives of estimation and
inference including a Frequentist approach using weighted least squares along
with bootstrap methods, and a Bayesian approach through both Markov chain Monte
Carlo and variational methods. We compare the estimation strategies mention
above with each other, and our radial kernel functions proposal with an
expansion based on regression spline, by means of an extensive simulation study
considering multiples scenarios in terms of sample size, number of repeated
measurements, and subject-specific correlation. Our experiments show that the
capabilities of our proposal based on radial kernel functions are indeed
comparable with or even better than those obtained from regression splines. We
illustrate our methodology by analyzing data from two AIDS clinical studies.
