# Hierarchical Shrinkage Priors for Regression Models

Griffin, Jim E., Brown, Philip J. (2017) Hierarchical Shrinkage Priors for Regression Models. Bayesian Analysis, 12 (1). pp. 135-159. ISSN 1936-0975. E-ISSN 1931-6690. (doi:10.1214/15-BA990)

PDF - Publisher pdf
 Preview
Official URL
http://dx.doi.org/10.1214/15-BA990

## Abstract

In some linear models, such as those with interactions, it is natural to include the relationship between the regression coefficients in the analysis. In this paper, we consider how robust hierarchical continuous prior distributions can be used to express dependence between the size but not the sign of the regression coefficients. For example, to include ideas of heredity in the analysis of linear models with interactions. We develop a simple method for controlling the shrinkage of regression effects to zero at different levels of the hierarchy by considering the behaviour of the continuous prior at zero. Applications to linear models with interactions and generalized additive models are used as illustrations.

Item Type: Article 10.1214/15-BA990 Faculties > Sciences > School of Mathematics Statistics and Actuarial ScienceFaculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics Jim Griffin 22 Mar 2017 11:50 UTC 01 Aug 2019 10:41 UTC https://kar.kent.ac.uk/id/eprint/60987 (The current URI for this page, for reference purposes) https://orcid.org/0000-0002-4828-7368