Dynamic estimation with random forests for discrete-time survival data

1 code implementation • 1 Mar 2021 • Hoora Moradian, Weichi Yao, Denis Larocque, Jeffrey S. Simonoff, Halina Frydman

Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available.

Methodology Applications

Ensemble methods for survival function estimation with time-varying covariates

2 code implementations • 31 May 2020 • Weichi Yao, Halina Frydman, Denis Larocque, Jeffrey S. Simonoff

We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L2 difference between the true and estimated survival functions.

On the Sensitivity of the Lasso to the Number of Predictor Variables

no code implementations • 18 Mar 2014 • Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff

Oracle inequalities provide probability loss bounds for the Lasso estimator at a deterministic choice of the regularization parameter.