PAC-Bayesian estimation and prediction in sparse additive models

Abstract

The present paper is about estimation and prediction in highdimensional additive models under a sparsity assumption (p ≫ n paradigm). A PAC-Bayesian strategy is investigated, delivering oracle inequalities in probability. The implementation is performed through recent outcomes in high-dimensional MCMC algorithms, and the performance of our method is assessed on simulated data.