Journal
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Volume 2014, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2014/857398
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Funding
- Macau Science and Technology Development Funds of Macau Special Administrative Region of the People's Republic of China [099/2013/A3]
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Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq (1/2 < q < 1) regularizations, to select key risk factors in the Cox's proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.
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