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
Original language | English |
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Article number | 107519 |
Number of pages | 17 |
Journal | Computational Statistics and Data Analysis |
Volume | 173 |
Early online date | 9 May 2022 |
DOIs | |
Publication status | Published - Sep 2022 |
Austrian Fields of Science 2012
- 101018 Statistics
Keywords
- Demmler-Reinsch basis
- Empirical Bayes
- Spectral density
- Stationary process
- KERNEL REGRESSION
- CHOICE
- OPTIMAL RATES
- MODELS
- CONVERGENCE
- BAYESIAN CREDIBLE SETS
- BANDWIDTH SELECTION