Regression Model

Use the drop-down menu to select a Bayesian method for the estimation of marker variables effects.

Available options are listed in the following table:

Option	Description
BayesA	Regression coefficients are assigned the Scaled-t density prior, which has higher mass at zero and thicker tails than the normal (Gaussian) density. The shrinkage of coefficient estimates is dependent on their effect-size.
BayesB	Regression coefficients are assigned a mixture of two finite mixture priors, a mixture of a point mass at zero and a Scaled-t density otherwise.
BayesC	Regression coefficients are assigned a mixture of two finite mixture priors, a mixture of a point mass at zero and a Gaussian density otherwise.
BayesianLASSO	Regression coefficients are assigned the Double Exponential (DE) density prior, which has higher mass at zero and thicker tails than the normal (Gaussian) density. The shrinkage of coefficient estimates is dependent on their effect-size.
BRR	Regression coefficients are assigned the Gaussian density prior, which induces shrinkage of coefficient estimates like in Ridge Regression.

To Specify the Regression Model:

Select the desired measure using the drop-down menu.