Adaptive Non-parametric Estimation of Mean and Autocovariance in Regression with Dependent Errors
Methodology
2021-08-19 v2 Statistics Theory
Applications
Statistics Theory
Abstract
Gaussian 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. Our 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 that accompanies the paper.
Keywords
Cite
@article{arxiv.1812.06948,
title = {Adaptive Non-parametric Estimation of Mean and Autocovariance in Regression with Dependent Errors},
author = {Tatyana Krivobokova and Paulo Serra and Francisco Rosales and Karolina Klockmann},
journal= {arXiv preprint arXiv:1812.06948},
year = {2021}
}