On selection and conditioning in multiple testing and selective inference
多重检验与选择性推断中的选择和条件化AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要属于通用多重检验与选择性推断理论，未明确对应所列领域细分。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
多重检验与选择性推断中的选择和条件化
- 研究问题
- 将数据驱动的假设选择、条件化推断和最终错误控制作为整体考察时，选择性推断与面向完整假设集合的多重检验有何功效关系？
- 方法与思路
- 作者建立统一理论，将选择及条件化步骤纳入同一程序，比较完整假设集合上的检验，并分析数据拆分等具体案例。
- 材料陈述的贡献
- 摘要称，直接定义于完整假设集合的多重检验方法，功效至少不低于相应选择条件化方法，且结论覆盖隐式无限假设集合。
- 阅读关注点
- 应核对功效比较与错误控制的精确定义、两类方法的对应构造，以及无限假设集合和各案例中功效提升的条件。
- 阅读边界
- 仅据所给 Crossref 摘要，不能将功效结论解释为任意现成多重检验都更优；具体比较条件与实施成本尚未说明。
原文摘要
Summary We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven collection of hypotheses is chosen from some large universe of hypotheses. Subsequently, inference takes place within this data-driven collection, conditioned on the information that was used for the selection. Examples of such methods include basic data splitting as well as modern data-carving methods and post-selection inference methods for lasso coefficients based on the polyhedral lemma. In this article, we take a holistic view of such methods, considering the selection, conditioning and final error control steps together as a single method. From this perspective, we demonstrate that multiple testing methods defined directly on the full universe of hypotheses are always at least as powerful as selective inference methods based on selection and conditioning. This result holds true even when the universe is potentially infinite and only implicitly defined, such as in the case of data splitting. We provide general theory and intuition before investigating in detail several case studies where a shift to a nonselective or unconditional perspective can yield a power gain.