Variable selection for survival models based on penalized likelihood / Seleção de covariáveis para modelos de sobrevivência via verossimilhança penalizada

AUTOR(ES)

Jony Arrais Pinto Junior

DATA DE PUBLICAÇÃO

2009

RESUMO

Variable selection is an important step when setting a parsimonious model. However, the most popular variable selection techniques, such as the best subset variable selection and the stepwise method, do not take into account inherent stochastic errors in the variable selection step. This work presents a study of alternative procedures to more popular methods for the Cox proportional hazards model and the frailty model. The alternative methods are based on penalized likelihood and differ from the usual variable selection methods, since their objective is to exclude from the model non significant variables, estimating their coefficient as zero. The resulting estimator has nice properties with appropriate choices of penalty functions and the tuning parameter. The assessment of these methods was studied through simulations, and an application to a real data set was considered.

ASSUNTO(S)

penalty functions penalized likelihood seleção de variáveis verossimilhança penalizada variable selection funções de penalidade

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