Statistics – Machine Learning
Scientific paper
2011-05-17
Statistics
Machine Learning
32 pages, 3 figures
Scientific paper
In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant features with high probability, even in ultra-high dimension. It is unclear whether this formal sure screening property is attainable when the response is a right-censored survival time. We propose a computationally very efficient independent screening method for survival data which can be viewed as the natural survival equivalent of correlation screening. We state conditions under which the method admits the sure screening property within a general class of single-index hazard rate models with ultra-high dimensional features. An iterative variant is also described which combines screening with penalized regression in order to handle more complex feature covariance structures. The methods are evaluated through simulation studies and through application to a real gene expression dataset.
Gorst-Rasmussen Anders
Scheike Thomas H.
No associations
LandOfFree
Independent screening for single-index hazard rate models with ultra-high dimensional features does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Independent screening for single-index hazard rate models with ultra-high dimensional features, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Independent screening for single-index hazard rate models with ultra-high dimensional features will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-727766