THE SOLUTION PATH OF THE GENERALIZED LASSO

• 成果类型: Article
• 署名作者: Tibshirani, Ryan J.; Taylor, Jonathan
• 署名单位: Stanford University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/11-AOS878
• 发表日期: 2011
• 页码: 1335-1371
• 关键词: smoothness; regression; selection

摘要

We present a path algorithm for the generalized lasso problem. This problem penalizes the l(1) norm of a matrix D times the coefficient vector, and has a wide range of applications, dictated by the choice of D. Our algorithm is based on solving the dual of the generalized lasso, which greatly facilitates computation of the path. For D = I (the usual lasso), we draw a connection between our approach and the well-known LARS algorithm. For an arbitrary D, we derive an unbiased estimate of the degrees of freedom of the generalized lasso fit. This estimate turns out to be quite intuitive in many applications.