Journals on Emergency Medicine - BJSTR Journal

Abstract

#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. High-dimensional data analysis has been a common and important topic in #biomedical/genomic/clinical studies. For example, the identification of #genetic factors for complex diseases such as lung cancer implicates a variety of genetic variants. For high-dimensional data, there is the well-known problem of curse of dimensionality arising in modeling. Therefore, variable selection is a fundamental task for high-dimensional statistical modeling. The "old school" way of doing variable selection is to follow a subset selection procedure prior to building the model of interest. The procedure commonly adopts AIC/BIC as evaluation metric and often iterates in a stepwise fashion. Yet this is independent of the subsequent modeling task hence the effectiveness might be less desirable. A more natural way is to integrate the variable selection into the modeling itself, i.e., the #penalized regression, which simultaneously performs variable selection and coefficient estimation. Theoretically, the "best" penalty for the penalized regression is the number of non-zero variables, to push as many variables to zero as possible.

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Optimization Techniques for SCAD Variable Selection in Medical Research by Yan Fang in BJSTR