This article proposes data-driven algorithms for fitting SEMIFAR models. The algorithms combine the data-driven estimation of the nonparametric trend and maximum likelihood estimation of the parameters. Convergence and asymptotic properties of the proposed algorithms are investigated. A large simulation study illustrates the practical performance of the methods.