Lasso Regression: Estimation and Shrinkage via the Limit of Gibbs Sampling
Lasso 回归：通过 Gibbs 抽样的极限实现估计与收缩
Summary The application of the lasso is espoused in high dimensional settings where only a small number of the regression coefficients are believed to be non-zero (i.e. the solution is sparse). Moreover, statistical properties of high dimensional lasso estimators are often proved under the assumption that the correlation between the predictors is bounded. In this vein, co-ordinatewise methods, which are the most common means of computing the lasso solution, naturally work…