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Bayesian Statistics 9$
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José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West

Print publication date: 2011

Print ISBN-13: 9780199694587

Published to Oxford Scholarship Online: January 2012

DOI: 10.1093/acprof:oso/9780199694587.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 23 April 2021

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction *

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction *

(p.501) Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction*
Bayesian Statistics 9

Nicholas G. Polson

James G. Scott

Oxford University Press

We study the classic problem of choosing a prior distribution for a location parameter β = (β 1,…, β p ) as p grows large. First, we study the standard “global‐local shrinkage” approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review some recent results showing how Lévy processes can be used to generate infinite‐dimensional versions of standard normal scale‐mixture priors, along with new priors that have yet to be seriously studied in the literature. This approach provides an intuitive framework both for generating new regularization penalties and shrinkage rules, and for performing asymptotic analysis on existing models.

Keywords:   Lévy Processes, Shrinkage, Sparsity

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