Tatyana KrivobokovaPaulo SerraFrancisco RosalesKarolina Klockmann2024-09-202024-09-202022-09-300167-9473https://doi.org/10.1016/j.csda.2022.107519https://cris.esan.edu.pe/handle/20.500.12640/376Gaussian processes that can be decomposed into a smooth mean function and a stationary autocorrelated noise process are considered and a fully automatic nonparametric method to simultaneous estimation of mean and auto-covariance functions of such processes is developed. The proposed empirical Bayes approach is data-driven, numerically efficient, and allows for the construction of confidence sets for the mean function. Performance is demonstrated in simulations and real data analysis. The method is implemented in the R package eBsc.Artículo de revistahttp://purl.org/coar/access_right/c_abf2