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Structuring shrinkage: some correlated priors for regression

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  • J. E. Griffin
  • P. J. Brown

Abstract

This paper develops a rich class of sparsity priors for regression effects that encourage shrinkage of both regression effects and contrasts between effects to zero whilst leaving sizeable real effects largely unshrunk. The construction of these priors uses some properties of normal-gamma distributions to include design features in the prior specification, but has general relevance to any continuous sparsity prior. Specific prior distributions are developed for serial dependence between regression effects and correlation within groups of regression effects. Copyright 2012, Oxford University Press.

Suggested Citation

  • J. E. Griffin & P. J. Brown, 2012. "Structuring shrinkage: some correlated priors for regression," Biometrika, Biometrika Trust, vol. 99(2), pages 481-487.
  • Handle: RePEc:oup:biomet:v:99:y:2012:i:2:p:481-487
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    File URL: http://hdl.handle.net/10.1093/biomet/asr082
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    Cited by:

    1. P. Richard Hahn & Carlos M. Carvalho, 2015. "Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 435-448, March.

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