Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
针对可分解为光滑均值函数和平稳自相关噪声过程的高斯过程，提出一种全自动非参数方法，同时估计均值函数和自协方差函数，并构建均值函数的置信集。
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