The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption ($p\gg n$ 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.
"PAC-Bayesian estimation and prediction in sparse additive models." Electron. J. Statist. 7 264 - 291, 2013. https://doi.org/10.1214/13-EJS771