Right-censored models by the expectile method
arxiv(2024)
摘要
Based on the expectile loss function and the adaptive LASSO penalty, the
paper proposes and studies the estimation methods for the accelerated failure
time (AFT) model. In this approach, we need to estimate the survival function
of the censoring variable by the Kaplan-Meier estimator. The AFT model
parameters are first estimated by the expectile method and afterwards, when the
number of explanatory variables can be large, by the adaptive LASSO expectile
method which directly carries out the automatic selection of variables. We also
obtain the convergence rate and asymptotic normality for the two estimators,
while showing the sparsity property for the censored adaptive LASSO expectile
estimator. A numerical study using Monte Carlo simulations confirms the
theoretical results and demonstrates the competitive performance of the two
proposed estimators. The usefulness of these estimators is illustrated by
applying them to three survival data sets.
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