Statistical Science

Bayesian Nonparametric Shrinkage Applied to Cepheid Star Oscillations

James Berger, William H. Jefferys, and Peter Müller

Full-text: Open access

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.

Article information

Source
Statist. Sci., Volume 27, Number 1 (2012), 3-10.

Dates
First available in Project Euclid: 14 March 2012

Permanent link to this document
https://projecteuclid.org/euclid.ss/1331729978

Digital Object Identifier
doi:10.1214/11-STS384

Mathematical Reviews number (MathSciNet)
MR2953491

Zentralblatt MATH identifier
1330.62186

Keywords
Nonparametric regression wavelets shrinkage prior sparsity variable selection methods

Citation

Berger, James; Jefferys, William H.; Müller, Peter. Bayesian Nonparametric Shrinkage Applied to Cepheid Star Oscillations. Statist. Sci. 27 (2012), no. 1, 3--10. doi:10.1214/11-STS384. https://projecteuclid.org/euclid.ss/1331729978


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