High-dimensional data have frequently been collected in many scientific areas including
genomewide association study, biomedical imaging, tomography, tumor classifications, and …

To accommodate the heterogeneity that is often present in ultrahigh-dimensional data, we
propose a conditional quantile screening method, which enables us to select features that …

It has become increasingly common for large-scale public data repositories and clinical
settings to have multiple types of data, including high-dimensional genomics, epigenomics …

The presented work is motivated by the need of reliably estimating and predicting the
survival rates for individuals diagnosed with cancer, when gene expression profiles are …

Single-index models are useful and fundamental tools for handling “curse of dimensionality”
problems in nonparametric regression. In addition to that, variable selection also plays an …

Desmedt, C., F. Piette, S. Loi, Y. Wang, F. Lallemand, B. Haibe-Kains, G. Viale, M. Delorenzi,
Y. Zhang, M. d'Assignies, et al.(2007). Strong time dependence of the 76-gene prognostic …

Survival data with ultrahigh dimensional covariates such as genetic markers have been
collected in medical studies and other fields. In this work, we propose a feature screening …

This paper presents improved methods for analysis of genome-wide association studies in
contemporary genetic research. Thanks to current sequencing methods, half to one million …

Technological advances that allow routine identification of high-dimensional risk factors
have led to high demand for statistical techniques that enable full utilization of these rich …

Much research has been devoted to developing variable selection methods for decades
since high dimensional data arise from many scientific and technological fields. Adopting …