Semivarying coefficient least-squares support vector regression for analyzing high-dimensional gene-environmental data

Jooyong Shim, Changha Hwang, Sunjoo Jeong and Insuk Sohn

Journal of Applied Statistics, 2018, vol. 45, issue 8, 1370-1381

Abstract: In the context of genetics and genomic medicine, gene-environment (G×E) interactions have a great impact on the risk of human diseases. Some existing methods for identifying G×E interactions are considered to be limited, since they analyze one or a few number of G factors at a time, assume linear effects of E factors, and use inefficient selection methods. In this paper, we propose a new method to identify significant main effects and G×E interactions. This is based on a semivarying coefficient least-squares support vector regression (LS-SVR) technique, which is devised by utilizing flexible semiparametric LS-SVR approach for censored survival data. This semivarying coefficient model is used to deal with the nonlinear effects of E factors. We also derive a generalized cross validation (GCV) function for determining the optimal values of hyperparameters of the proposed method. This GCV function is also used to identify significant main effects and G×E interactions. The proposed method is evaluated through numerical studies.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:45:y:2018:i:8:p:1370-1381

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DOI: 10.1080/02664763.2017.1371676

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