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.
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