Guarding against Spurious Discoveries in High Dimensions
防范高维数据中的虚假发现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。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
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：摘要讨论通用高维变量筛选、偶然拟合与模型选择，未提供足以归入所列微观、宏观或金融细分方向的直接依据。
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
防范高维数据中的虚假发现
- 研究问题
- 高维数据中，变量子集的大量搜索可能产生偶然拟合；如何建立统计基准，判断所发现的关联是否超出随机机会？
- 方法与思路
- 作者定义零模型下最优变量子集的虚假拟合优度，提出 LAMM 计算算法，并以乘子自助法估计其渐近分布。
- 材料陈述的贡献
- 摘要称，该指标推广最大虚假相关，可为广义线性模型和绝对损失回归提供虚假发现基准，并用于筛选候选模型。
- 阅读关注点
- 应核对零模型如何定义、维度和子集大小的增长条件、协方差估计要求，以及自助法有效性与算法计算精度的条件。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明检验误差控制的具体保证，应用也不能确证因果关联。
原文摘要
Many data mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can easily arise in high-dimensional data analysis due to enormous possibilities of such selections. How can we know statistically our discoveries better than those by chance? In this paper, we define a measure of goodness of spurious fit, which shows how good a response variable can be fitted by an optimally selected subset of covariates under the null model, and propose a simple and effective LAMM algorithm to compute it. It coincides with the maximum spurious correlation for linear models and can be regarded as a generalized maximum spurious correlation. We derive the asymptotic distribution of such goodness of spurious fit for generalized linear models and $L_1$ regression. Such an asymptotic distribution depends on the sample size, ambient dimension, the number of variables used in the fit, and the covariance information. It can be consistently estimated by multiplier bootstrapping and used as a benchmark to guard against spurious discoveries. It can also be applied to model selection, which considers only candidate models with goodness of fits better than those by spurious fits. The theory and method are convincingly illustrated by simulated examples and an application to the binary outcomes from German Neuroblastoma Trials.