A New Hyperprior Distribution for Bayesian Regression Model with Application in Genomics

Abstract

In the regression analysis, there are situations where the model have more predictor variables than observations of dependent variable, resulting in the problem known as \"large p small n\". In the last fifteen years, this problem has been received a lot of attention, specially in the genome-wide context. Here we purposed the bayes H model, a bayesian regression model using mixture of two scaled inverse chi square as hyperprior distribution of variance for each regression coefficient. This model is implemented in the R package BayesH.