Contact
 Laboratoire Jean Kuntzmann
 IMAG  Bureau 144 
 700, avenue centrale
 38401 St Martin d'Hères
 +33.(0)4.57.42.17.41
 Didier.Girard@imag.fr
Research
Knowledge_Dissemination

Didier A. Girard homepage
POSITION
CNRS Researcher,
at LJK laboratory, D.A.T.A. Department,
IPS team.
Principal Investigator for several CNRSCEA Projects, including :
Nonparametric estimation by multidimensional spline.
THEMES
General Theory and Methodology, Computational Statistics, Inverse Problems, Inference for Stochastic Processes.
keywords:
adaptive tuning; multidimensional smoothing spline; biasvariance tradeoff; generalized crossvalidation; radial basis functions; numerical methods for large data sets; variational data assimilation; tomography; randomized trace; simulation based inference; measurement errors; Matern process; infill asymptotics.
SELECTED PUBLICATIONS
 Optimal regularized reconstruction in computerized tomography. SIAM J. Scientific and Statistical Computing, vol. 8, 6, pp. 934950, 1987.
 Un algorithme rapide pour le calcul de la trace de l'inverse d'une grande matrice. Research report RR665M, TIM3_IMAG (1987).pdf. The correlatedsampling extension suggested here has proved useful for heteroskedastic cases, as in "Letters to the Editor: Comment on O'Sullivan" JASA, 88(424), 1993, p. 14781479.
 A fast 'MonteCarlo crossvalidation' procedure for large least squares problems with noisy data. RR 687M, TIM3_IMAG (1987); and
Numer. Math., vol. 56, pp. 123, 1989.
 Asymptotic optimality of the fast randomized versions of GCV and CL in ridge regression and regularization. The Annals of Statistics, vol. 19, 4, pp. 19501963, 1991.
 The fast MonteCarlo crossvalidation and CL procedures: comments, new results and application to image recovery problems (with discussion by seven authors and a rejoinder). Computational Statistics, vol. 10, pp. 205258, 1995.
 The minimum "recontructionerror" choice of regularization parameters: some more efficient methods and their application to deconvolution problems. SIAM J. Scientific and Statistical Computing, vol. 16, pp. 13871403, 1995. (coauthor L. Desbat)
 Asymptotic comparison of (partial) crossvalidation, GCV and randomized GCV in nonparametric regression. The Annals of Statistics, vol. 26, pp. 315334, 1998.

Estimating the accuracy of (local) crossvalidation via randomised GCV choices in kernel or smoothing spline regression. J. Nonparametric Statistics.
vol. 22, pp. 4164, 2010.

Efficiently estimating some common geostatistical models by 'energyvariance matching' or its randomized 'conditionalmean' versions.
Spatial Statistics.
Available online 23 March 2017.
http://dx.doi.org/10.1016/j.spasta.2017.01.001.

Asymptotic NearEfficiency of the 'GibbsEnergy and EmpiricalVariance' Estimating Functions for Fitting Matern Models, I: Densely sampled processes,
& II: Accounting for measurement errors via conditional GE mean.
Statistics and Probability Letters & https://arxiv.org/pdf/0909.1046v3.pdf.
