Quadratic approximation on SCAD penalized estimation

Sunghoon Kwona, Hosik Choi, Yongdai Kima

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish
Pages (from-to)421-428
Number of pages8
JournalComputational Statistics and Data Analysis
Issue number1
StatePublished - 1 Jan 2011


  • Penalized approach
  • Quadratic approximation
  • SCAD
  • Variable selection


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