ArticleBiometrika2017
Partition-based ultrahigh-dimensional variable screening.
Article in Biometrika, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Group Structure Guided Ultra-High Dimensional Feature Screening for Survival Outcome.Statistics in medicine · 2026Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- The Kendall interaction filter for variable interaction screening in high dimensional classification problems.Journal of applied statistics · 2023Article
- Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.Statistica Sinica · 2022Article
- Clustering of longitudinal interval-valued data via mixture distribution under covariance separability.Journal of applied statistics · 2020Article
- Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review.Applied mathematics : a journal of Chinese universities · 2017Article
- A selective overview of feature screening methods with applications to neuroimaging data.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Traditional variable selection methods are compromised by overlooking useful information on covariates with similar functionality or spatial proximity, and by treating each covariate independently. Leveraging prior grouping information on covariates, we propose partition-based screening methods for ultrahigh-dimensional variables in the framework of generalized linear models. We show that partition-based screening exhibits the sure screening property with a vanishing false selection rate, and we propose a data-driven partition screening framework with unavailable or unreliable prior knowledge on covariate grouping and investigate its theoretical properties. We consider two special cases: correlation-guided partitioning and spatial location- guided partitioning. In the absence of a single partition, we propose a theoretically justified strategy for combining statistics from various partitioning methods. The utility of the proposed methods is demonstrated via simulation and analysis of functional neuroimaging data.
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Registered trials
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