Reconstruction of Sparse Vectors in White Gaussian Noise

We consider the problem of reconstruction of a sparse vector observed against a background of white Gaussian noise. The sparsity is assumed to be unknown. Two approaches to statistical estimation in this case are discussed, namely, the model selection method and threshold estimators. We propose a method of selecting a threshold estimator based on the principle of empirical complexity minimization with minimal conservative penalization.