ArticleScience China. Mathematics2015
A selective overview of feature screening for ultrahigh-dimensional data.
Article in Science China. Mathematics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.PLoS computational biology · 2026Article
- Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review.Genes · 2023Review
- Quantile forward regression for high-dimensional survival data.Lifetime data analysis · 2023Article
- Sure Joint Screening for High Dimensional Cox's Proportional Hazards Model Under the Case-Cohort Design.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression.Bioinformatics (Oxford, England) · 2022Article
- Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification.Frontiers in genetics · 2022Article
- On correlation rank screening for ultra-high dimensional competing risks data.Journal of applied statistics · 2022Article
- Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems.Journal of the American Statistical Association · 2022Article
- Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques.Scientific reports · 2021Article
- The Lq- NORM LEARNING FOR ULTRAHIGH-DIMENSIONAL SURVIVAL DATA: AN INTEGRATIVE FRAMEWORK.Statistica Sinica · 2020Article
- Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection.Scientific reports · 2020Article
- Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening.BMC bioinformatics · 2020Article
- FEATURE SELECTION FOR GENERALIZED VARYING COEFFICIENT MIXED-EFFECT MODELS WITH APPLICATION TO OBESITY GWAS.The annals of applied statistics · 2020Article
- Feature Screening in Ultrahigh Dimensional Generalized Varying-coefficient Models.Statistica Sinica · 2020Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- A quantile regression forest based method to predict drug response and assess prediction reliability.PloS one · 2018Article
- An adaptive threshold determination method of feature screening for genomic selection.BMC bioinformatics · 2017Article
- Model-Free Conditional Independence Feature Screening For Ultrahigh Dimensional Data.Science China. Mathematics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many high-dimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.