Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear
models. Correlation-based variable screening, in which one leverages marginal correlations …

Imbalanced data, in which the data exhibit an unequal or highly-skewed distribution
between its classes/categories, are pervasive in many scientific fields, with application range …

This study develops a marginal screening test to detect the presence of significant predictors
for a right-censored time-to-event outcome under a high-dimensional accelerated failure …

This article is concerned with a non-marginal feature screening procedure for varying
coefficient competing risks models with ultra-high dimensional covariates. The proposed …

In an ultra-high dimensional setting with a huge number of covariates, variable screening is
useful for dimension reduction before applying a more refined method for model selection …

Prognosis plays a pivotal role in patient management and trial design. A useful prognostic
model should correctly identify important risk factors and estimate their effects. In this article …

Motivated by multi-center biomedical studies that cannot share individual data due to privacy
and ownership concerns, we develop communication-efficient iterative distributed …

We consider varying coefficient Cox models with high-dimensional covariates. We apply the
group Lasso to these models and propose a variable selection procedure. Our procedure …

In this paper, we propose a new measure, called Projection Quantile Correlation (PQC), to
detect quantile dependence between a response and multivariate predictors at a given …

We propose a new, simple, model-free conditional screening approach for survival data.
Sure screening and consistency in ranking properties are rigorously established, and …