发表机构
The University of Texas at El Paso（德克萨斯大学埃尔帕索分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对超高维变量选择中边际筛选可能遗漏弱信号活跃预测因子的问题，提出结构化筛选与选择（S3VS）迭代框架，结合结果筛选与相关局部集，在多种模型下实现，模拟和卵巢癌数据表明其可改善变量恢复或预测。
AI 中文摘要
超高维数据（其中p远大于n）在基因组学和生物医学研究中很常见。边际筛选可能会遗漏具有弱边际信号的活跃预测因子，尤其是在预测因子强相关的情况下。我们开发了结构化筛选与选择变量选择（S3VS），这是一个迭代框架，将基于结果的筛选与基于相关的局部预测因子集相结合。在每次迭代中，S3VS识别主要变量，通过预测因子关联形成局部集，应用模型特定的选择器，聚合已选择和未选择的变量，并更新候选集，以及在适当时更新结果表示。该框架允许灵活的主要变量、局部集和聚合规则，并提供了线性、广义线性、加速失效时间和Cox模型的实现。对于指定的单步线性配置，我们在代理覆盖、相关性分离、集内保留和活跃保留聚合的条件下建立了确定筛选性质。模拟研究比较了完整和首次迭代的S3VS与线性、逻辑和Cox设置中的单遍SIS程序。当相关预测因子提供有用的代理信息时，S3VS可以改善变量恢复或预测，尽管收益取决于预测因子结构和选择器的选择。在卵巢癌数据中，使用临床变量的完整S3VS显示出最强的内部区分度和早期预测能力，而使用临床变量的SIS-Cox-LASSO显示出最强的外部区分度。两种分子方法均未持续降低预测误差，且没有基因在所有五个外部折中被选中。S3VS提供了一个灵活的框架，用于在模型特定选择之前利用预测因子依赖性。该方法已在CRAN R包S3VS中实现。
英文摘要
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.
