Bayesian Analysis

Comment on Article by Finegold and Drton

Babak Shahbaba

Full-text: Open access

Abstract

Scale mixtures of normals have been discussed extensively in the literature as heavy-tailed alternatives to the normal distribution for robust modeling. They have been used either as error models to handle outliers or as prior distributions to provide more reasonable shrinkage of model parameters. The proposed method by Finegold and Drton goes beyond the existing literature both in terms of application (graphical models) and methodology (Dirichlet t) for outlier handling. While this approach can be applied to many other problems, in this discussion I will focus on its application in Bayesian modeling of high throughput biological data.

Article information

Source
Bayesian Anal., Volume 9, Number 3 (2014), 557-560.

Dates
First available in Project Euclid: 5 September 2014

Permanent link to this document
https://projecteuclid.org/euclid.ba/1409921104

Digital Object Identifier
doi:10.1214/14-BA899

Mathematical Reviews number (MathSciNet)
MR3256054

Zentralblatt MATH identifier
1327.62162

Keywords
Robust Bayesian modeling Scale mixtures of normals High dimensional problems

Citation

Shahbaba, Babak. Comment on Article by Finegold and Drton. Bayesian Anal. 9 (2014), no. 3, 557--560. doi:10.1214/14-BA899. https://projecteuclid.org/euclid.ba/1409921104


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References

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See also

  • Related item: Michael Finegold, Mathias Drton. Robust Bayesian Graphical Modeling Using Dirichlet t-Distributions. Bayesian Anal., Vol. 9, Iss. 3 (2014) 521–550.