We consider the problem of estimating independent and possibly high–dimensional normal means vectors in a sparse empirical Bayes framework that glues together a recent manifold–modelling technique called diffusion maps, with a more classical concept of sparsity based on the assumption that most of the unknown coordinates of model parameter are actually 0.
Diffusion driven empirical Bayes estimation of high-dimensional Normal Means vectors / Brutti, Pierpaolo. - STAMPA. - (2009), pp. 103-107. (Intervento presentato al convegno S.Co. 2009 tenutosi a Milano).
Diffusion driven empirical Bayes estimation of high-dimensional Normal Means vectors
BRUTTI, Pierpaolo
2009
Abstract
We consider the problem of estimating independent and possibly high–dimensional normal means vectors in a sparse empirical Bayes framework that glues together a recent manifold–modelling technique called diffusion maps, with a more classical concept of sparsity based on the assumption that most of the unknown coordinates of model parameter are actually 0.File allegati a questo prodotto
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