.png){kind=icon}
Return
p-Values for High-Dimensional Regression
.png){kind=icon}
OA
AI
DOI：10.1198/jasa.2009.tm08647.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder that splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables, using the data from the second split. This yields asymptotic error control under minimal conditions. This involves a one-time random split of the data, however. Results are sensitive to this arbitrary choice, which amounts to a p-value lottery and makes it difficult to reproduce results. Here we show that inference across multiple random splits can be aggregated while maintaining asymptotic control over the inclusion of noise variables. We show that the resulting p-values can be used for control of both family-wise error and false discovery rate. In addition, the proposed aggregation is shown to improve power while reducing the number of falsely selected variables substantially.
Keywords:
Data splitting
False discovery rate
Family-wise error rate
High-dimensional variable selection
Multiple comparisons
Journal
J
IF:
3
Papers:
5.2K
Citations:
4.8W
Organization
Cited Papers
ShareSaveShareSaveLASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSaveShareSave
View more