GLOBAL TESTING UNDER SPARSE ALTERNATIVES: ANOVA, MULTIPLE COMPARISONS AND THE HIGHER CRITICISM
GLOBAL TESTING UNDER SPARSE ALTERNATIVES: ANOVA, MULTIPLE COMPARISONS AND THE HIGHER CRITICISM
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DOI：
10.1214/11-aos910
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发表时间：
2011-10-01
影响因子：
4.5
通讯作者：
Plan, Yaniv
中科院分区：
文献类型：
--
作者：
Arias-Castro, Ery;Candes, Emmanuel J.;Plan, Yaniv
测试在线性模型中回归系数的一部分（统计分析的主食）至少可以追溯到引入方差分析（ANOVA）的工作。在假设系数矢量稀疏的假设下，我们研究了这个问题，这是现代高维环境中的常见情况。假设我们有p协变量，并且在替代方案下，响应仅取决于p（1-alpha）的顺序，0
Testing for the significance of a subset of regression coefficients in a linear model, a staple of statistical analysis, goes back at least to the work of Fisher who introduced the analysis of variance (ANOVA). We study this problem under the assumption that the coefficient vector is sparse, a common situation in modern high-dimensional settings. Suppose we have p covariates and that under the alternative, the response only depends upon the order of p(1-alpha) of those, 0