Adaptive Covariance Estimation with model selection

We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud

Non parametric estimation of smooth stationary covariance functions by interpolation methods

Covariance estimation, Geostatistics, Positive definite, Spline interpolation, Stationary processes,