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June 2011 Bayesian estimation of intensity surfaces on the sphere via needlet shrinkage and selection
James G. Scott
Bayesian Anal. 6(2): 307-327 (June 2011). DOI: 10.1214/11-BA611


This paper describes an approach for Bayesian modeling in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding, and Bayesian local shrinkage rules. We study the performance of the proposed methodology both on simulated data and on two real data sets: one involving the cosmic microwave background radiation, and one involving the reconstruction of a global news intensity surface inferred from published Reuters articles in August, 1996. The fully Bayesian approach based on robust, sparse shrinkage priors seems to outperform other alternatives.


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James G. Scott. "Bayesian estimation of intensity surfaces on the sphere via needlet shrinkage and selection." Bayesian Anal. 6 (2) 307 - 327, June 2011.


Published: June 2011
First available in Project Euclid: 13 June 2012

MathSciNet: MR2806246
zbMATH: 1330.62221
Digital Object Identifier: 10.1214/11-BA611

Primary: 62H11
Secondary: ‎42C40, 62F15, 62H12, 62J07, 62M40, 62P25

Rights: Copyright © 2011 International Society for Bayesian Analysis


Vol.6 • No. 2 • June 2011
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