Structured Screen-and-Select for Ultra-High-Dimensional Variable Selection
超高维变量选择的结构化筛选与选择AI 解读 ↓
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超高维变量选择的结构化筛选与选择
- 研究问题
- 在变量数远超样本量且预测变量高度相关时，如何借助变量间关联减少边际筛选遗漏弱信号变量的问题？
- 方法与思路
- 作者提出S3VS迭代框架，结合结局筛选、相关性局部变量集、模型专用选择器与聚合规则，并更新候选集及适用的结局表示。
- 材料陈述的贡献
- 摘要对特定单步线性配置给出保留全部活跃变量的筛选保证；模拟收益依赖相关结构和选择器，癌症应用的内外部表现并不一致。
- 阅读关注点
- 应核对理论保证覆盖的具体配置、代理覆盖等条件，以及完整迭代的验证、外部评估、预测误差和入选变量的稳定性。
- 阅读边界
- 这是arXiv预印本，不能据此声称已同行评审或期刊接收；单步线性配置的理论保证不等于全部迭代与所有模型均有保证。
原文摘要
Ultra-high-dimensional data, with p far exceeding n, are common in genomics and biomedical research. Marginal screening can miss active predictors with weak marginal signals, especially under strong predictor correlation. We develop Structured Screen-and-Select Variable Selection (S3VS), an iterative framework that combines outcome-based screening with correlation-based local predictor sets. At each iteration, S3VS identifies leading variables, forms local sets through predictor associations, applies a model-specific selector, aggregates selected and nonselected variables, and updates the candidate set and, when appropriate, the outcome representation. The framework allows flexible leading-variable, local-set, and aggregation rules, with implementations for linear, generalized linear, accelerated failure-time, and Cox models. For a specified one-step linear configuration, we establish sure screening under conditions on proxy coverage, correlation separation, within-set retention, and active-preserving aggregation. Simulations compare full and first-iteration S3VS with one-pass SIS procedures in linear, logistic, and Cox settings. S3VS can improve variable recovery or prediction when correlated predictors provide useful proxy information, although gains depend on predictor structure and selector choice. In ovarian-cancer data, full S3VS with clinical variables showed the strongest internal discrimination and early prediction, whereas SIS--Cox--LASSO with clinical variables showed the strongest external discrimination. Neither molecular approach consistently reduced prediction error, and no gene was selected in all five outer folds. S3VS provides a flexible framework for exploiting predictor dependence before model-specific selection. The method is implemented in the CRAN R package S3VS.