In data sets with many more features than observations, independent screening based on all
univariate regression models leads to a computationally convenient variable selection …

The paper considers the problem of hypothesis testing and confidence intervals in high
dimensional proportional hazards models. Motivated by a geometric projection principle, we …

In this article, we propose a model-free conditional feature screening method with false
discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built …

Big data is ubiquitous in various fields of sciences, engineering, medicine, social sciences,
and humanities. It is often accompanied by a large number of variables and features. While …

This paper discusses statistical methods for estimating complex correlation structure from
large pharmacogenomic datasets. We selectively review several prominent statistical …

Forecasting survival risks for time-to-event data is an essential task in clinical research.
Practitioners often rely on well-structured statistical models to make predictions for patient …

Motivated by ultrahigh-dimensional biomarkers screening studies, we propose a model-free
screening approach tailored to censored lifetime outcomes. Our proposal is built upon the …

Background This study is to explore the key genes and signaling transduction pathways
related to the survival time of glioblastoma multiforme (GBM) patients. Results Our results …

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in
gaining better insights into the biological influences on the disease and has become a …

Ultra-high dimensional data with right censored survival times are frequently collected in
large-scale biomedical studies, for which feature screening has become an indispensable …