*Corresponding author:Yan Fang, School of Finance, Shanghai University of International Business and Economics, Shanghai, China
Received: August 15, 2018; Published: August 23, 2018
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High-dimensional data analysis requires variable selection to identify truly relevant variables. More often it is done implicitly via regularization, such as penalized regression. Of the many versions of penalties, SCAD has shown good properties and has been widely adopted in medical research and many more areas. This paper reviews the various optimization techniques in solving SCAD penalized regression.
Abbreviations: LQA: Local Quadratic Approximation; LLA: Local Linear Approximation; DCA: Difference Convex Algorithm; SOCP: Second Order Cone Programming; ADMM: Alternating Direction Method of Multipliers