Adaptive Inference for the Mean of a Gaussian Process in Functional Data
函数型数据中高斯过程均值的自适应推断
Summary We propose and analyse fully data-driven methods for inference about the mean function of a Gaussian process from a sample of independent trajectories of the process, observed at random time points and corrupted by additive random error. Our methods are based on thresholded least squares estimators relative to an approximating function basis. The variable threshold levels are determined from the data and the resulting estimates adapt to the unknown sparsity of the…