Résumé : Model selection with high-dimensional data becomes an important issue in the last two decades. With the presence of missing data, only a few methods are available to select a model, and their performances are limited. We propose a novel approach – Adaptive Bayesian SLOPE, as an extension of sorted l1 regularization but in Bayesian framework, to perform parameter estimation and variable selection simultaneously in high-dimensional setting. This methodology in particular aims at controlling the False Discovery Rate (FDR). Meanwhile, we tackle the problem of missing data with a stochastic approximation EM algorithm. The proposed methodology is further illustrated by comprehensive simulation studies, in terms of power, FDR and bias of estimation.
Lieu : Salle 316 - LMB