7 research outputs found
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,