Lower bounds on bayes factors for a linear regression model

This paper develops a Bayesian analysis of testing linear parameter restictions in a normal regression model. Instead of searching for a suitable "objective" prior for such a test, several interesting classes of informative priors are postulated, and the lower bounds on the Bayes factors are derived.