Principled sure independence screening for Cox models with ultra-high-dimensional covariates
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چکیده
منابع مشابه
Principled sure independence screening for Cox models with ultra-high-dimensional covariates
It is rather challenging for current variable selectors to handle situations where the number of covariates under consideration is ultra-high. Consider a motivating clinical trial of the drug bortezomib for the treatment of multiple myeloma, where overall survival and expression levels of 44760 probesets were measured for each of 80 patients with the goal of identifying genes that predict survi...
متن کامل6 Sure Independence Screening for Ultra - High Dimensional Feature Space ∗
High dimensionality is a growing feature in many areas of contemporary statistics. Variable selection is fundamental to high-dimensional statistical modeling. For problems of large or huge scale pn, computational cost and estimation accuracy are always two top concerns. In a seminal paper, Candes and Tao (2007) propose a minimum l1 estimator, the Dantzig selector, and show that it mimics the id...
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Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear models. Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge. Prior works on correlation-based variable screening either impose strong statistical priors on...
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A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS...
متن کاملDiscussion of "Sure Independence Screening for Ultra-High Dimensional Feature Space.
June 30, 2008 Abstract Variable selection plays an important role in high dimensional statistical modeling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, estimation accuracy and computational cost are two top concerns. In a recent paper, Candes and Tao (2007) propose the Dantzig selector using L1 regularization...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2012
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2011.08.002