文章基本信息

标题：PAC-Bayesian estimation and prediction in sparse additive models
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作者：Benjamin Guedj ; Pierre Alquier
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2013
卷号：7
页码：264-291
DOI：10.1214/13-EJS771
语种：English
出版社：Institute of Mathematical Statistics
摘要：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.