@article {Silva102244, author = {Renato Rodrigues Silva}, title = {A New Hyperprior Distribution for Bayesian Regression Model with Application in Genomics}, elocation-id = {102244}, year = {2017}, doi = {10.1101/102244}, publisher = {Cold Spring Harbor Laboratory}, 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 {\textquotedblleft}large p small n{\textquotedblright}. 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.}, URL = {https://www.biorxiv.org/content/early/2017/01/22/102244}, eprint = {https://www.biorxiv.org/content/early/2017/01/22/102244.full.pdf}, journal = {bioRxiv} }