Variable selection with error control: another look at stability selection
نویسندگان
چکیده
منابع مشابه
Variable selection with error control: another look at stability selection
Stability selection was recently introduced by Meinshausen and Bühlmann 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 varia...
متن کاملHigh Dimensional Variable Selection with Error Control
Background. The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to ...
متن کاملAnother look at medical error.
Medical error continues to be a topic of discussion. Blaming the physician or nurse for error is too simplistic and may serve to blur larger system problems from being identified and addressed. This article considers recent history of assignment of errors from a quality assurance perspective, multiple paths which result in error, reviewing the 1999 Institute of Medicine report and looking beyon...
متن کاملStability Selection for Structured Variable Selection
In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated when used in conjunction with the lasso and orthogonal matching pursuit algor...
متن کاملConsistent selection of tuning parameters via variable selection stability
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model sparsity. Existing tuning criteria mainly follow the route of minimizing the e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2012
ISSN: 1369-7412
DOI: 10.1111/j.1467-9868.2011.01034.x