Alternative Global – Local Shrinkage Priors Using Hypergeometric – Beta Mixtures

@inproceedings{Polson2009AlternativeG,
  title={Alternative Global – Local Shrinkage Priors Using Hypergeometric – Beta Mixtures},
  author={Nicholas G. Polson and James G. Scott},
  year={2009}
}
This paper introduces an approach to estimation in possibly sparse data sets using shrinkage priors based upon the class of hypergeometric-beta distributions. These widely applicable priors turn out to be a four-parameter generalization of the beta family, and are pseudo-conjugate: they cannot themselves be expressed in closed form, but they do yield tractable moments and marginal likelihoods when used as priors for the mean of a normal distribution. These priors are useful in situations where… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 28 references

Bayes minimax estimators of the mean of a scale mixture of multivariate normal distributions

  • D. Fourdrinier, O. Kortbi, W. Strawderman
  • Journal of Multivariate Analysis,
  • 2008
Highly Influential
5 Excerpts

A generalization of generalized beta distributions

  • M. B. Gordy
  • Technical report, Board of Governors of the…
  • 1998
Highly Influential
8 Excerpts

On the construction of Bayes minimax estimators

  • D. Fourdrinier, W. Strawderman, M. T. Wells
  • The Annals of Statistics,
  • 1998
Highly Influential
9 Excerpts

Table of Integrals, Series, and Products

  • I. Gradshteyn, I. Ryzhik
  • 1965
Highly Influential
5 Excerpts

A robust generalized Bayes estimator and confidence region for a multivariate normal mean

  • J. O. Berger
  • The Annals of Statistics,
  • 1980
Highly Influential
10 Excerpts

Estimation of the mean of a multivariate normal distribution

  • C. Stein
  • The Annals of Statistics,
  • 1981
Highly Influential
4 Excerpts

Proper Bayes minimax estimators of the multivariate normal mean

  • W. Strawderman
  • The Annals of Statistics,
  • 1971
Highly Influential
5 Excerpts

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