Adaptive basis selection for functional data analysis via stochastic penalization

we propose an adaptive method of analyzing a collection of curves which can be, individually, modeled as a linear combination of spline basis functions. through the introduction of latent bernoulli variables, the number of basis functions, the variance of the error measurements and the coefficients of the expansion are determined. we provide a modification of the stochastic em algorithm for which numerical results show that the estimates are very close to the true curve in the sense of l2 norm.