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Optimization Techniques for SCAD Variable Selection in Medical Research

Volume 8 - Issue 2

Yan Fang1*, YanYan Kong2 and Yumei Jiao3

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    • 1School of Finance, Shanghai University of International Business and Economics, China
    • 2PET Center, Fudan University, China
    • 3Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, China

    *Corresponding author: Yan Fang, School of Finance, Shanghai University of International Business and Economics, Shanghai, China

Received: August 15, 2018;   Published: August 23, 2018

DOI: 10.26717/BJSTR.2018.08.001632

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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

Abstract | Introduction | Optimization Techniques for SCAD | Conclusion | References |