Large classes of proper priors for linear models
Bruno Sansó, Luis R. Pericchi · Communication in Statistics- Theory and Methods · 1994
We consider the problem of a hierarchical linear model with normal likelihood and variance known in the case where there is very little information about the hyperparameters. We address the problem proposing the use of a very wide class of priors that can be expressed as scale mixtures of normals. Two possibilities are considered: a class based on Double Exponential densities and a class based on Cauchy densities. An example is given to illustrate the techniques.