ArticleJournal of the American Statistical Association2016
Conditional Sure Independence Screening.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- A Group Structure Guided Ultra-High Dimensional Feature Screening for Survival Outcome.Statistics in medicine · 2026Article
- A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.PLoS computational biology · 2026Article
- Global Group Testing and Screening With Dynamic Effects.Statistica Sinica · 2026Article
- Evaluating Methods for High-Dimensional Mediation in Metabolomics Data.Environmental science & technology · 2026Article
- Article
- Uncertainty Quantification in Epigenetic Clocks via Conformalized Quantile Regression.Genetic epidemiology · 2025Article
- Uncertainty quantification in epigenetic clocks via conformalized quantile regression.medRxiv : the preprint server for health sciences · 2025Article
- Conditional Variable Screening for Ultra-High Dimensional Longitudinal Data With Time Interactions.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Joint Screening for Ultra-High Dimensional Multi-Omics Data.Bioengineering (Basel, Switzerland) · 2024Article
- Are Latent Factor Regression and Sparse Regression Adequate?Journal of the American Statistical Association · 2024Article
- Image response regression via deep neural networks.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2023Article
- Quantile forward regression for high-dimensional survival data.Lifetime data analysis · 2023Article
- Gene Screening for Prognosis of Non-Muscle-Invasive Bladder Carcinoma under Competing Risks Endpoints.Cancers · 2023Article
- Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.Statistica Sinica · 2022Article
- High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression.Bioinformatics (Oxford, England) · 2022Article
- Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease.Journal of the American Statistical Association · 2022Article
- Conditional screening for ultrahigh-dimensional survival data in case-cohort studies.Lifetime data analysis · 2021Article
- A NewJournal of computational biology : a journal of computational molecular cell biology · 2021Article
- Genome-wide association studies of brain imaging data via weighted distance correlation.Bioinformatics (Oxford, England) · 2020Article
- Consistent Estimation of Generalized Linear Models with High Dimensional Predictors via Stepwise Regression.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Independence screening is powerful for variable selection when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or its variants. When some prior knowledge on a certain important set of variables is available, a natural assessment on the relative importance of the other predictors is their conditional contributions to the response given the known set of variables. This results in conditional sure independence screening (CSIS). CSIS produces a rich family of alternative screening methods by different choices of the conditioning set and can help reduce the number of false positive and false negative selections when covariates are highly correlated. This paper proposes and studies CSIS in generalized linear models. We give conditions under which sure screening is possible and derive an upper bound on the number of selected variables. We also spell out the situation under which CSIS yields model selection consistency and the properties of CSIS when a data-driven conditioning set is used. Moreover, we provide two data-driven methods to select the thresholding parameter of conditional screening. The utility of the procedure is illustrated by simulation studies and analysis of two real datasets.
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