The knockoff-based multiple testing setup of Barber & Candes (2015) for variable selection in multiple regression where sample size is as large as the number of explanatory variables is considered. The method of Benjamini & Hochberg (1995) based on ordinary least squares estimates of the regression coefficients is adjusted to the setup, transforming it to a valid p-value based false discovery rate controlling method not relying on any specific correlation structure of the explanatory variables. Simulations and real data applications show that our proposed method that is agnostic to {\pi}0, the proportion of unimportant explanatory variables, and a data-adaptive version of it that uses an estimate of {\pi}0 are powerful competitors of the false discovery rate controlling method in Barber & Candes (2015).
翻译:Barber & Candes (2015年) 的基于传导的多重测试设置, 用于在多个回归中选择变量, 样本大小与解释变量数量一样大。 Benjami & Hochberg (1995年) 的方法基于对回归系数的普通最低方位估算, 被调整为设置, 将其转化为有效的基于虚假发现率的控制方法, 而不依赖于解释变量的具体相关结构 。 模拟和真实数据应用显示, 我们提出的方法对于 {pin} 0 具有不可知性, 不重要的解释变量的比例, 以及使用估计 {pin}0 的数据适应性版本, 是Barber 和 Candes (2015年) 虚假发现率控制方法的强大竞争者 。