Quadratic approximation on SCAD penalized estimation

Authors:
Sunghoon Kwon;Hosik Choi;Yongdai Kim
Affiliations:
Seoul National University, Republic of Korea;Hoseo University, Republic of Korea;Seoul National University, Republic of Korea
Venue:
Computational Statistics & Data Analysis
Year:
2011

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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.