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2013 PAC-Bayesian estimation and prediction in sparse additive models
Benjamin Guedj, Pierre Alquier
Electron. J. Statist. 7: 264-291 (2013). DOI: 10.1214/13-EJS771

Abstract

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.

Citation

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Benjamin Guedj. Pierre Alquier. "PAC-Bayesian estimation and prediction in sparse additive models." Electron. J. Statist. 7 264 - 291, 2013. https://doi.org/10.1214/13-EJS771

Information

Published: 2013
First available in Project Euclid: 24 January 2013

zbMATH: 1337.62075
MathSciNet: MR3020421
Digital Object Identifier: 10.1214/13-EJS771

Subjects:
Primary: 62G08, 62J02, 65C40

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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