Statistical methods for genome-wide association studies (GWAS) continue to improve.
However, the increasing volume and variety of genetic and genomic data make …

The simultaneous estimation and variable selection for Cox model has been discussed by
several authors when one observes right-censored failure time data. However, there does …

Some interesting recent studies have shown that neural network models are useful
alternatives in modeling survival data when the assumptions of a classical parametric or …

Objective: To explore the application of the Cox model based on extreme learning machine
in the survival analysis of patients with chronic heart failure. Methods: The medical records …

For massive survival data, we propose a subsampling algorithm to efficiently approximate
the estimates of regression parameters in the additive hazards model. We establish …

When large amounts of survival data arrive in streams, conventional estimation methods
become computationally infeasible since they require access to all observations at each …

Variable selection for interval‐censored failure time data has recently attracted a great deal
of attention along with the analysis of interval‐censored data in both method developments …

Semi-competing risks data arise when both non-terminal and terminal events are
considered in a model. Such data with multiple events of interest are frequently encountered …

The threshold regression model is an effective alternative to the Cox proportional hazards
regression model when the proportional hazards assumption is not met. This paper …

Along with the developing technology, it has become easier to collect data and store it,
which causes an increase in the number of data dimensions. The number of dimensions …