Joint non-parametric estimation of mean and auto-covariances for Gaussian processes

Tatyana Krivobokova, Paulo Serra, Francisco Rosales and Karolina Klockmann

Computational Statistics & Data Analysis, 2022, vol. 173, issue C

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. 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.1

Keywords: Demmler-Reinsch basis; Empirical Bayes; Spectral density; Stationary process (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:173:y:2022:i:c:s0167947322000998

DOI: 10.1016/j.csda.2022.107519

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