Abstract:
|
Analyzing data in a Bayesian framework has the advantage of including subjective prior knowledge in the model. Examples include imposing realistic constraints on the parameters, including scientific knowledge, or incorporating results from previous studies such as in a meta-analysis. However, implementing a subjective Bayesian approach in practice proves difficult, particularly when modeling multiple parameters as in regression. We propose a Bayesian linear model by placing a prior directly on the coefficient of determination instead of the regression coefficients, allowing researchers to easily include information through a univariate parameter. We also propose using the model as a shrinkage prior for regularization, handling sparsity and high-dimensional modeling.
|