Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

Predicting the occurrence of a particular event of interest at future time points is the primary
goal of survival analysis. The presence of incomplete observations due to time limitations or …

High resolution microarrays and second-generation sequencing platforms are powerful tools
to investigate genome-wide alterations in DNA copy number, methylation, and gene …

In high throughput genomic studies, an important goal is to identify a small number of
genomic markers that are associated with development and progression of diseases. A …

In the presence of high-dimensional predictors, it is challenging to develop reliable
regression models that can be used to accurately predict future outcomes. Further …

Use of microarray technology often leads to high-dimensional and low-sample size (HDLSS)
data settings. A variety of approaches have been proposed for variable selection in this …

The accelerated failure time (AFT) models have proved useful in many contexts, though
heavy censoring (as for example in cancer survival) and high dimensionality (as for example …

Background When boosting algorithms are used for building survival models from high-
dimensional data, it is common to fit a Cox proportional hazards model or to use least …

High‐dimensional data with censored outcomes of interest are prevalent in medical
research. To analyze such data, the regularized Buckley–James estimator has been …

Quantile regression has emerged as a powerful tool in survival analysis as it directly links
the quantiles of patients' survival times to their demographic and genomic profiles …