Articles

Chagny G., Channarond A., Hoang V., Roche A. (2022), Adaptive nonparametric estimation of a component density in a two-class mixture model, Journal of Statistical Planning and Inference, vol. 216, p. 51-69

Roche A. (2022), New perspectives in smoothing : minimax estimation of the mean and principal components ofdiscretized functional data, The Graduate Journal of Mathematics, vol. 7, n°2, p. 95-107

Bitseki Penda S., Roche A. (2020), Local bandwidth selection for kernel density estimation in a bifurcating Markov chain model, Journal of Nonparametric Statistics, vol. 32, n°3, p. 535-562

Roche A. (2018), Local Optimization of Black-Box Function with High or Infinite-Dimensional Inputs, Computational Statistics, vol. 33, n°1, p. 467–485

Chagny G., Comte F., Roche A. (2017), Adaptive estimation of the hazard rate with multiplicative censoring, Journal of Statistical Planning and Inference, vol. 184, p. 25-47

Communications sans actes

Sautreuil M., Bérard C., Chagny G., Channarond A., Roche A., Vergne N. (2018), Modèle de mélange binomial négatif bivarié pour l'analyse de données RNA-Seq, SFds 50èmes Journées de Statistique, Paris, France

Prépublications / Cahiers de recherche

Chagny G., Meynaoui A., Roche A. (2022), Adaptive nonparametric estimation in the functional linear model with functional output, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 39 p.

Roche A. (2022), Lasso in infinite dimension : application to variable selection in functional multivariate linear regression, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 43 p.

Belhakem M., Picard F., Rivoirard V., Roche A. (2021), Minimax estimation of Functional Principal Components from noisy discretized functional data, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 35 p.

Roche A. (2018), Variable selection and estimation in multivariate functional linear regression via the Lasso, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 21 p.