3 citations · 4 across the 4 of their papers we have counts for
6 papers · 1 filter
Asymptotic analysis of maximum likelihood estimation of covariance parameters for Gaussian processes: an introduction with proofs
François Bachoc
This article provides an introduction to the asymptotic analysis of covariance parameter estimation for Gaussian processes. Maximum likelihood estimation is considered. The aim of…
Rate of convergence for geometric inference based on the empirical Christoffel function
Mai Trang Vu, François Bachoc, Edouard Pauwels
We consider the problem of estimating the support of a measure from a finite, independent, sample. The estimators which are considered are constructed based on the empirical Christ…
Composite likelihood estimation for a gaussian process under fixed domain asymptotics
François Bachoc, Moreno Bevilacqua, Daira Velandia
We study the problem of estimating the covariance parameters of a one-dimensional Gaussian process with exponential covariance function under fixed-domain asymptotics. We show that…
On the Post Selection Inference constant under Restricted Isometry Properties
François Bachoc, Gilles Blanchard, Pierre Neuvial
Uniformly valid confidence intervals post model selection in regression can be constructed based on Post-Selection Inference (PoSI) constants. PoSI constants are minimal for orthog…
On the smallest eigenvalues of covariance matrices of multivariate spatial processes
François Bachoc, Reinhard Furrer
There has been a growing interest in providing models for multivariate spatial processes. A majority of these models specify a parametric matrix covariance function. Based on obser…
Asymptotic properties of multivariate tapering for estimation and prediction
R. Furrer, F. Bachoc, J. Du
Parameter estimation for and prediction of spatially or spatio--temporally correlated random processes are used in many areas and often require the solution of a large linear syste…