In this work, we propose and explore a novel network-constraint survival methodology considering the Weibull accelerated failure time (AFT) model combined with a penalized likelihood approach for variable selection and estimation [2]. Our estimator explicitly incorporates the correlation patterns among predictors using a double penalty that promotes both sparsity and the grouping effect. In or- der to solve the structured sparse regression problems we present an efficient iterative computational algorithm based on proximal gradient descent method [1]. We establish the theoretical consistency of the proposed estimator and moreover, we evaluate its performance both on synthetic and real data examples.
A network-constrain Weibull AFT model based on proximal gradient descent method
Daniela De Canditiis;Italia De Feis;
2023
Abstract
In this work, we propose and explore a novel network-constraint survival methodology considering the Weibull accelerated failure time (AFT) model combined with a penalized likelihood approach for variable selection and estimation [2]. Our estimator explicitly incorporates the correlation patterns among predictors using a double penalty that promotes both sparsity and the grouping effect. In or- der to solve the structured sparse regression problems we present an efficient iterative computational algorithm based on proximal gradient descent method [1]. We establish the theoretical consistency of the proposed estimator and moreover, we evaluate its performance both on synthetic and real data examples.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
