Independent screening for single‐index hazard rate models with ultrahigh dimensional features
By Anders Gorst-rasmussen, Thomas H. Scheike. 2011
102 citations recorded by the source corpus.
Summary. 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 ultrahigh 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 class of single‐index hazard rate models with ultrahigh dimensional features and describe the generally detrimental effect of censoring on performance. An iterative variant of the method is also described which combines screening with penalized regression to handle more complex feature covariance structures. The methodology is evaluated through simulation studies and through application to a real gene expression data set.
Subfields: Genetics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c5480254692abad185ba08cc7dd4a28013b64625
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In Genetics
In Statistics and Probability
- Thomas H. Scheike
- John P. Klein
- H. V. van Houwelingen
- Joseph G. Ibrahim
- Chenlei Leng
- Xiangyu Wang
- Anders Gorst-rasmussen
- Heng Lian
Citations in the corpus
- Handbook of survival analysis (2014, background): “Recently Gorst-Rasmussen and Scheike (2012) took the same model to propose a sure independence screening method that shows good performance.”
- High dimensional ordinary least squares projection for screening variables (2015, background): “…has been extended, for example, to generalized linear models (Fan and Fan, 2008; Fan, et al., 2009; Fan and Song, 2010), additive models (Fan, et al., 2011), hazard regression (Zhao and Li, 2012; Gorst-Rasmussen and Scheike, 2013), and to accommodate conditional correlation (Barut et al., 2012).”
- Conditional quantile screening in ultrahigh-dimensional heterogeneous data (2015, unknown): “…have been studied extensively in various ultrahigh-dimensional contexts, such as generalized linear models (Fan & Song, 2010), additive models (Fan et al., 2011), Cox proportional hazards regression with survival data (Zhao & Li, 2012), and additive hazard models (Gorst-Rasmussen & Scheike, 2013).”
- Coordinate Descent Methods for the Penalized Semiparametric Additive Hazards Model (2012, unknown): “For example, we can take f(n) := log n (Gorst-Rasmussen and Scheike 2011).”
- Targeting the LOX/hypoxia axis reverses many of the features that make pancreatic cancer deadly: inhibition of LOX abrogates metastasis and enhances drug efficacy (2015, unknown): “Probes were variance-filtered, collapsed into per-gene transcripts by selecting the highest variance probe for each gene and filtered for univariate survival association using a 1,000,000-round permutation test based on the FAST statistic (Gorst-Rasmussen & Scheike, 2012).”