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arXiv · 1812.06948

Adaptive Non-parametric Estimation of Mean and Autocovariance in Regression with Dependent Errors

Tatyana Krivobokova·Paulo Serra·Francisco Rosales·Karolina Klockmann
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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.

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Tatyana Krivobokova, Paulo Serra, Francisco Rosales, Karolina Klockmann. 2018-12-17. Adaptive Non-parametric Estimation of Mean and Autocovariance in Regression with Dependent Errors. https://arxiv.org/abs/1812.06948

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