Research interests

During my PhD thesis and two postdoctoral positions, I worked on different research topics, from theoretical to more applied statistics. Here is a non-exhaustive list of my research areas:

  • Nonparametric adaptive tests of independence,

  • Kernel methods for testing independence,

  • Methods for Global Sensitivity Analysis,

  • Nonparametric adaptive estimation,

  • Functional data,

  • Causal reasoning,

  • Causality for time series.

Publications

  • Albert M., Laurent B., Marrel A. and Meynaoui A. Adaptive test of independence based on HSIC measures, 2021 [HAL]. To appear in Annals of Statistics.

Pre-publications

  • Meynaoui A., Marrel A. and Laurent B. New statistical methodology for second level global sensitivity analysis, 2019 [HAL]. In minor revision.

  • Chagny G., Meynaoui A., and Roche A. Adaptive nonparametric estimation in the functional linear model with functional output, 2022 [HAL]. Submitted.

  • Meynaoui A., Assaad C.K., Devijver E., Gaussier E., and Gössler G. Identification in time series summary causal graphs, 2022. Submitted (preprint is coming soon).

Communications

(*) : By video-conference.

PhD Thesis

Title: New developments around dependence measures for sensitivity analysis : application to severe accident studies for generation IV reactors.

Supervisors : Béatrice Laurent and Amandine Marrel.

Date of award: November 22, 2019.

Thesis manuscript is available here.