Lasso regression: estimation and shrinkage via the limit of Gibbs sampling
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 well in the presence of...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of the Royal Statistical Society: Series B (Statistical Methodology)|
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