We consider the single-index model estimation problem from a sparsity perspective using a PAC-Bayesian approach. On the theoretical side, we offer a sharp oracle inequality, which is more powerful than the best known oracle inequalities for other common procedures of single-index recovery. The proposed method is implemented by means of the reversible jump Markov chain Monte Carlo technique and its performance is compared with that of standard procedures. Lien vers l'article
ALQUIER, P. and BIAU, G. (2013). Sparse Single-Index Model. Journal of Machine Learning Research, 14, pp. 243-280.