Knot selection for regression splines via the LASSO

被引：0
|
作者：
Osborne, MR [1 ]
Presnell, B [1 ]
Turlach, BA [1 ]
机构：
[1] Australian Natl Univ, Ctr Math & Applicat, Canberra, ACT 0200, Australia
来源：
DIMENSION REDUCTION, COMPUTATIONAL COMPLEXITY AND INFORMATION | 1998年 / 30卷
关键词：
convex programming; dual problem; knot selection; regression splines;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Tibshirani (1996) proposes the "Least Absolute Shrinkage and Selection Operator" (lasso) as a method for regression estimation which combines features of shrinkage and variable selection. In this paper we present an algorithm that allows efficient calculation of the lasso estimator. In particular our algorithm can also be used when the number of variables exceeds the number of observations. This algorithm is then applied to the problem of knot selection for regression splines.
引用
收藏
页码：44 / 49
页数：6
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