Ultra-high dimensional data sets often need a screening step that removes irrelevant
variables prior to the main analysis. In high-dimensional linear regression, screening …

With the availability of high-dimensional genetic biomarkers, it is of interest to identify
heterogeneous effects of these predictors on patients' survival, along with proper statistical …

Variable screening methods have been shown to be effective in dimension reduction under
the ultra-high dimensional setting. Most existing screening methods are designed to rank the …

With the explosion of ultrahigh dimensional data in various fields, many sure independent
screening methods have been proposed to reduce the dimensionality of data from a large …

Modern biomedical studies have yielded abundant survival data with high-throughput
predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of …

Abstract Motivation Linear mixed models (LMMs) have long been the method of choice for
risk prediction analysis on high-dimensional data. However, it remains computationally …

For ultrahigh-dimensional data, sure independent screening methods can effectively reduce
the dimensionality while ensuring that all the active variables can be retained with high …

A model-free feature screening method for ultrahigh-dimensional right-censored data is
advocated. A two-step approach, with the help of knockoff features, is proposed to specify …

Forward regression, a classical variable screening method, has been widely used for model
building when the number of covariates is relatively low. However, forward regression is …

Background Building prognostic models of clinical outcomes is an increasingly important
research task and will remain a vital area in genomic medicine. Prognostic models of clinical …