PAC-Bayesian estimation and prediction in sparse additive models

The present paper is about estimation and prediction in high-dimensional 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. Lien vers l'article

GUEDJ, B. and ALQUIER, P. (2013). PAC-Bayesian estimation and prediction in sparse additive models. The Electronic Journal of Statistics, 7, pp. 264-291.

Mots clés : #Additive-models, #MCMC, #Oracle-inequality, #PAC, #Bayesian-bounds, #Regression-estimation, #Sparsity, #stochastic-search