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Pac-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights


  • Pierre Alquier


  • Karim Lounici



We consider the sparse regression model where the number of parametersp is larger than the sample size n. The difficulty when consideringhigh-dimensional problems is to propose estimators achieving a good compromisebetween statistical and computational performances. The BIC estimatorfor instance performs well from the statistical point of view [11] but can onlybe computed for values of p of at most a few tens. The Lasso estimator issolution of a convex minimization problem, hence computable for large valueof p. However stringent conditions on the design are required to establish fastrates of convergence for this estimator. Dalalyan and Tsybakov [19] proposea method achieving a good compromise between the statistical and computationalaspects of the problem. Their estimator can be computed for reasonablylarge p and satisfies nice statistical properties under weak assumptions on thedesign. However, [19] proposes sparsity oracle inequalities in expectation forthe empirical excess risk only. In this paper, we propose an aggregation proceduresimilar to that of [19] but with improved statistical performances. Ourmain theoretical result is a sparsity oracle inequality in probability for the trueexcess risk for a version of exponential weight estimator. We also propose aMCMC method to compute our estimator for reasonably large values of p.

Suggested Citation

  • Pierre Alquier & Karim Lounici, 2010. "Pac-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights," Working Papers 2010-40, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2010-40

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