Journal
BIOMETRIKA
Volume 99, Issue 2, Pages 481-487Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asr082
Keywords
Fused prior; Grouped prior; Lasso; Multiple regression; Normal-gamma prior; Sparsity
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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.
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