Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
417680 | Computational Statistics & Data Analysis | 2011 | 8 Pages |
Abstract
In this paper, we propose a method of quadratic approximation that unifies various types of smoothly clipped absolute deviation (SCAD) penalized estimations. For convenience, we call it the quadratically approximated SCAD penalized estimation (Q-SCAD). We prove that the proposed Q-SCAD estimator achieves the oracle property and requires only the least angle regression (LARS) algorithm for computation. Numerical studies including simulations and real data analysis confirm that the Q-SCAD estimator performs as efficient as the original SCAD estimator.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Sunghoon Kwon, Hosik Choi, Yongdai Kim,