Variable Selection in Ultra-high Dimensional Feature Space for the Cox
Model with Interval-Censored Data
Variable Selection in Ultra-high Dimensional Feature Space for the Cox
Model with Interval-Censored Data
We develop a set of variable selection methods for the Cox model under interval censoring, in the ultra-high dimensional setting where the dimensionality can grow exponentially with the sample size. The methods select covariates via a penalized nonparametric maximum likelihood estimation with some popular penalty functions, including lasso, adaptive lasso, …