Sparse Estimation by Exponential Weighting

Sparse Estimation by Exponential Weighting

Consider a regression model with fixed design and Gaussian noise where the regression function can potentially be well approximated by a function that admits a sparse representation in a given dictionary. This paper resorts to exponential weights to exploit this underlying sparsity by implementing the principle of sparsity pattern aggregation. This model selection take on sparse estimation allo...

Exponential Screening and Optimal Rates of Sparse Estimation By

In high-dimensional linear regression, the goal pursued here is to estimate an unknown regression function using linear combinations of a suitable set of covariates. One of the key assumptions for the success of any statistical procedure in this setup is to assume that the linear combination is sparse in some sense, for example, that it involves only few covariates. We consider a general, nonne...

Improved Channel Estimation for DVB-T2 Systems by Utilizing Side Information on OFDM Sparse Channel Estimation

The second generation of digital video broadcasting (DVB-T2) standard utilizes orthogonal frequency division multiplexing (OFDM) system to reduce and to compensate the channel effects by utilizing its estimation. Since wireless channels are inherently sparse, it is possible to utilize sparse representation (SR) methods to estimate the channel. In addition to sparsity feature of the channel, the...

PAC-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights

We consider the sparse regression model where the number of parameters p is larger than the sample size n. The difficulty when considering high-dimensional problems is to propose estimators achieving a good compromise between statistical and computational performances. The BIC estimator for instance performs well from the statistical point of view [11] but can only be computed for values of p o...

Aggregation by Exponential Weighting and Sharp Oracle Inequalities