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February 2012 Bayesian Nonparametric Shrinkage Applied to Cepheid Star Oscillations
James Berger, William H. Jefferys, Peter Müller
Statist. Sci. 27(1): 3-10 (February 2012). DOI: 10.1214/11-STS384

Abstract

Bayesian nonparametric regression with dependent wavelets has dual shrinkage properties: there is shrinkage through a dependent prior put on functional differences, and shrinkage through the setting of most of the wavelet coefficients to zero through Bayesian variable selection methods. The methodology can deal with unequally spaced data and is efficient because of the existence of fast moves in model space for the MCMC computation.

The methodology is illustrated on the problem of modeling the oscillations of Cepheid variable stars; these are a class of pulsating variable stars with the useful property that their periods of variability are strongly correlated with their absolute luminosity. Once this relationship has been calibrated, knowledge of the period gives knowledge of the luminosity. This makes these stars useful as “standard candles” for estimating distances in the universe.

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James Berger. William H. Jefferys. Peter Müller. "Bayesian Nonparametric Shrinkage Applied to Cepheid Star Oscillations." Statist. Sci. 27 (1) 3 - 10, February 2012. https://doi.org/10.1214/11-STS384

Information

Published: February 2012
First available in Project Euclid: 14 March 2012

zbMATH: 1330.62186
MathSciNet: MR2953491
Digital Object Identifier: 10.1214/11-STS384

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.27 • No. 1 • February 2012
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