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Density estimation with quadratic loss: a confidence intervals method

Pierre Alquier (2008)

ESAIM: Probability and Statistics

We propose a feature selection method for density estimation with quadratic loss. This method relies on the study of unidimensional approximation models and on the definition of confidence regions for the density thanks to these models. It is quite general and includes cases of interest like detection of relevant wavelets coefficients or selection of support vectors in SVM. In the general case, we prove that every selected feature actually improves the performance of the estimator. In the case...

Density methods and results in approximation theory

Allan Pinkus (2004)

Banach Center Publications

Approximation theory and functional analysis share many common problems and points of contact. One of the areas of mutual interest is that of density results. In this paper we briefly survey various methods and results in this area starting from work of Weierstrass and Riesz, and extending to more recent times.

Density questions in the classical theory of moments

Christian Berg, J. P. Reus Christensen (1981)

Annales de l'institut Fourier

Let μ be a positive Radon measure on the real line having moments of all orders. We prove that the set P of polynomials is note dense in L p ( R , μ ) for any p > 2 , if μ is indeterminate. If μ is determinate, then P is dense in L p ( R , μ ) for 1 p 2 , but not necessarily for p > 2 . The compact convex set of positive Radon measures with same moments as μ is studied in some details.

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