Bayesian Lasso Prior. Bayesian approaches to variable and model selection have been developed and applied with some success to high dimensional data Brown et al. PriorMdl lassoblm NumPredictors creates a Bayesian linear regression model object PriorMdl composed of NumPredictors predictors and an intercept and sets the NumPredictors property.
The lasso prior is the Bayesian equivalent to the LASSO method for performing variable selection Park. The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the priors on the regression parameters are indepen-dent double-exponential Laplace distributions. The mode produces exact zeros and sparsity.
Bayesian framework and selection of prior distributions We adopt a variance-inflation model for outliers as follows.
The benefit was submitting the prior information on top of the quality generalized linear regression model. The first was to improve prediction accuracy and the second was to improve model interpretation by determining a smaller subset. The GLM with logit link can be expressed as. Posterior mean is not sparse zero Choosing the shrinkage penalty.
