We address the problem of constructing varying-coefficient models based on
basis expansions along with the technique of regularization. A crucial point in
our modeling procedure is the selection of smoothing parameters in the
regularization method. In order to choose the parameters objectively, we derive
model selection criteria from the viewpoints of information-theoretic and
Bayesian approach. We demonstrate the effectiveness of proposed modeling
strategy through Monte Carlo simulations and analyzing a real data set.