PAC-Bayesian estimation and prediction in sparse additive models. Guedj, B. & Alquier, P. Electron. J. Statist., 7:264–291, The Institute of Mathematical Statistics and the Bernoulli Society, 2013.
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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.

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