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We study the performance of estimators of a sparse nonrandom vector based on an observation which is linearly transformed and corrupted by additive white Gaussian noise. Using the reproducing kernel Hilbert space framework, we derive a new lower bound on the estimator variance for a given differentiable bias function (including the unbiased case) and an almost arbitrary transformation matrix (including the underdetermined case considered in compressed sensing theory). For the special case of a sparse vector corrupted by white Gaussian noise-i.e., without a linear transformation-and unbiased estimation, our lower bound improves on previously proposed bounds.Index Terms-Sparsity, parameter estimation, sparse linear model, denoising, variance bound, reproducing kernel Hilbert space, RKHS.

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We prove the existence of series ∑anψn, whose coefficients (an) are in ∩p>1ℓp and whose terms (ψn) are translates by rational vectors in double-struckRd of a family of approximations to the identity, having the property that the partial sums are dense in various spaces of functions such as Wiener’s algebra W(C0,ℓ1), Cb(Rd), C0(Rd), Lp(Rd), for every p∈[1,∞), and the space of measurable functions. Applying this theory to particular situations, we establish approximations by such series to solutions of the heat and Laplace equations as well as to probability density functions.

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