Iterative Plug-In Algorithms for SEMIFAR Models—Definition, Convergence, and Asymptotic Properties

Data-driven algorithms for fitting SEMIFAR models are proposed that combine the data-driven estimation of the nonparametric trend and maximum likelihood estimate of the parameters.

Abstract

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.