.png){kind=icon}
Return
Variable selection with error control: another look at stability selection
.png){kind=icon}
OA
AI
DOI：10.1111/j.1467-9868.2011.01034.x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Stability selection was recently introduced by Meinshausen and Buhlmann as a very general technique designed to improve the performance of a variable selection algorithm. It is based on aggregating the results of applying a selection procedure to subsamples of the data. We introduce a variant, called complementary pairs stability selection, and derive bounds both on the expected number of variables included by complementary pairs stability selection that have low selection probability under the original procedure, and on the expected number of high selection probability variables that are excluded. These results require no (e.g. exchangeability) assumptions on the underlying model or on the quality of the original selection procedure. Under reasonable shape restrictions, the bounds can be further tightened, yielding improved error control, and therefore increasing the applicability of the methodology.
Keywords:
Complementary pairs stability selection
r-concavity
Subagging
Subsampling
Variable selection
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveComparison of discrimination methods for the classification of tumors using gene expression data
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more