Comput Math Methods Med - Novel harmonic regularization approach for variable selection in Cox's proportional hazards model.

Tópicos

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Resumo

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.

Resumo Limpo

variabl select import issu regress number variabl select method propos involv nonconvex penalti function paper investig novel harmon regular method can approxim nonconvex lq q regular select key risk factor cox proport hazard model use microarray gene express data harmon regular method can effici solv use propos direct path seek approach can produc solut close approxim convex loss function nonconvex regular simul result base artifici dataset four real microarray gene express dataset real diffus larg bcell lymphoma dcbcl lung cancer aml dataset show harmon regular method can accur variabl select exist lasso seri method

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