A Homotopy Algorithm for the Quantile Regression Lasso and Related Piecewise Linear Problems
We show that the homotopy algorithm of Osborne, Presnell, and Turlach (2000), which has proved such an effective optimal path following method for implementing Tibshirani's "lasso" for variable selection in least squares estimation problems, can be extended to polyhedral objectives in examples such as the quantile regression lasso. The new algorithm introduces the novel feature that it requires two homotopy sequences involving continuation steps with respect to both the constraint bound and the...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Computational and Graphical Statistics|
|01_Osborne_A_Homotopy_Algorithm_for_the_2011.pdf||202.21 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.